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8c757059
编写于
12月 04, 2018
作者:
J
JiabinYang
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' of
https://github.com/PaddlePaddle/Paddle
into optimize_hs_op
, test=develop
上级
b387a194
6d04a9cf
变更
40
展开全部
隐藏空白更改
内联
并排
Showing
40 changed file
with
1840 addition
and
244 deletion
+1840
-244
cmake/external/ngraph.cmake
cmake/external/ngraph.cmake
+6
-12
cmake/inference_lib.cmake
cmake/inference_lib.cmake
+9
-0
paddle/fluid/API.spec
paddle/fluid/API.spec
+2
-1
paddle/fluid/framework/CMakeLists.txt
paddle/fluid/framework/CMakeLists.txt
+2
-1
paddle/fluid/framework/details/op_registry.h
paddle/fluid/framework/details/op_registry.h
+3
-17
paddle/fluid/framework/ir/is_test_pass.cc
paddle/fluid/framework/ir/is_test_pass.cc
+1
-1
paddle/fluid/framework/ir/is_test_pass_tester.cc
paddle/fluid/framework/ir/is_test_pass_tester.cc
+2
-2
paddle/fluid/framework/ir/mkldnn_placement_pass.cc
paddle/fluid/framework/ir/mkldnn_placement_pass.cc
+1
-1
paddle/fluid/framework/ir/node.cc
paddle/fluid/framework/ir/node.cc
+25
-1
paddle/fluid/framework/ir/node.h
paddle/fluid/framework/ir/node.h
+12
-0
paddle/fluid/framework/ngraph_bridge.cc
paddle/fluid/framework/ngraph_bridge.cc
+85
-3
paddle/fluid/framework/ngraph_bridge.h
paddle/fluid/framework/ngraph_bridge.h
+6
-8
paddle/fluid/framework/ngraph_operator.cc
paddle/fluid/framework/ngraph_operator.cc
+339
-8
paddle/fluid/framework/ngraph_operator.h
paddle/fluid/framework/ngraph_operator.h
+1
-6
paddle/fluid/framework/op_info.h
paddle/fluid/framework/op_info.h
+0
-7
paddle/fluid/framework/operator.cc
paddle/fluid/framework/operator.cc
+6
-0
paddle/fluid/framework/operator.h
paddle/fluid/framework/operator.h
+5
-0
paddle/fluid/framework/type_defs.h
paddle/fluid/framework/type_defs.h
+0
-2
paddle/fluid/inference/api/demo_ci/CMakeLists.txt
paddle/fluid/inference/api/demo_ci/CMakeLists.txt
+11
-1
paddle/fluid/operators/sequence_ops/sequence_mask_op.h
paddle/fluid/operators/sequence_ops/sequence_mask_op.h
+4
-6
paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.cc
...e/fluid/operators/sigmoid_cross_entropy_with_logits_op.cc
+6
-0
paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.h
...le/fluid/operators/sigmoid_cross_entropy_with_logits_op.h
+65
-26
paddle/fluid/operators/yolov3_loss_op.cc
paddle/fluid/operators/yolov3_loss_op.cc
+221
-0
paddle/fluid/operators/yolov3_loss_op.h
paddle/fluid/operators/yolov3_loss_op.h
+483
-0
paddle/scripts/paddle_build.sh
paddle/scripts/paddle_build.sh
+3
-1
python/paddle/fluid/clip.py
python/paddle/fluid/clip.py
+8
-8
python/paddle/fluid/executor.py
python/paddle/fluid/executor.py
+9
-8
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+3
-2
python/paddle/fluid/layers/detection.py
python/paddle/fluid/layers/detection.py
+109
-0
python/paddle/fluid/layers/io.py
python/paddle/fluid/layers/io.py
+12
-1
python/paddle/fluid/layers/learning_rate_scheduler.py
python/paddle/fluid/layers/learning_rate_scheduler.py
+8
-7
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+61
-53
python/paddle/fluid/metrics.py
python/paddle/fluid/metrics.py
+16
-15
python/paddle/fluid/param_attr.py
python/paddle/fluid/param_attr.py
+2
-1
python/paddle/fluid/tests/test_detection.py
python/paddle/fluid/tests/test_detection.py
+13
-0
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+2
-1
python/paddle/fluid/tests/unittests/test_sigmoid_cross_entropy_with_logits_op.py
...ts/unittests/test_sigmoid_cross_entropy_with_logits_op.py
+34
-0
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py
+215
-0
python/paddle/fluid/transpiler/distribute_transpiler.py
python/paddle/fluid/transpiler/distribute_transpiler.py
+36
-35
python/setup.py.in
python/setup.py.in
+14
-9
未找到文件。
cmake/external/ngraph.cmake
浏览文件 @
8c757059
...
...
@@ -32,6 +32,8 @@ IF(NOT ${WITH_NGRAPH})
return
()
ENDIF
()
INCLUDE
(
GNUInstallDirs
)
INCLUDE
(
ExternalProject
)
SET
(
NGRAPH_PROJECT
"extern_ngraph"
)
...
...
@@ -40,10 +42,14 @@ SET(NGRAPH_GIT_TAG "f9fd9d4cc318dc59dd4b68448e7fbb5f67a28bd0")
SET
(
NGRAPH_SOURCES_DIR
${
THIRD_PARTY_PATH
}
/ngraph
)
SET
(
NGRAPH_INSTALL_DIR
${
THIRD_PARTY_PATH
}
/install/ngraph
)
SET
(
NGRAPH_INC_DIR
${
NGRAPH_INSTALL_DIR
}
/include
)
SET
(
NGRAPH_LIB_DIR
${
NGRAPH_INSTALL_DIR
}
/
${
CMAKE_INSTALL_LIBDIR
}
)
SET
(
NGRAPH_SHARED_LIB_NAME libngraph.so.
${
NGRAPH_VERSION
}
)
SET
(
NGRAPH_CPU_LIB_NAME libcpu_backend.so
)
SET
(
NGRAPH_TBB_LIB_NAME libtbb.so.2
)
SET
(
NGRAPH_GIT_REPO
"https://github.com/NervanaSystems/ngraph.git"
)
SET
(
NGRAPH_SHARED_LIB
${
NGRAPH_LIB_DIR
}
/
${
NGRAPH_SHARED_LIB_NAME
}
)
SET
(
NGRAPH_CPU_LIB
${
NGRAPH_LIB_DIR
}
/
${
NGRAPH_CPU_LIB_NAME
}
)
SET
(
NGRAPH_TBB_LIB
${
NGRAPH_LIB_DIR
}
/
${
NGRAPH_TBB_LIB_NAME
}
)
ExternalProject_Add
(
${
NGRAPH_PROJECT
}
...
...
@@ -63,18 +69,6 @@ ExternalProject_Add(
CMAKE_ARGS -DMKLDNN_LIB_DIR=
${
MKLDNN_INSTALL_DIR
}
/lib
)
if
(
UNIX AND NOT APPLE
)
include
(
GNUInstallDirs
)
SET
(
NGRAPH_LIB_DIR
${
NGRAPH_INSTALL_DIR
}
/
${
CMAKE_INSTALL_LIBDIR
}
)
else
()
SET
(
NGRAPH_LIB_DIR
${
NGRAPH_INSTALL_DIR
}
/lib
)
endif
()
MESSAGE
(
STATUS
"nGraph lib will be installed at:
${
NGRAPH_LIB_DIR
}
"
)
SET
(
NGRAPH_SHARED_LIB
${
NGRAPH_LIB_DIR
}
/
${
NGRAPH_SHARED_LIB_NAME
}
)
SET
(
NGRAPH_CPU_LIB
${
NGRAPH_LIB_DIR
}
/
${
NGRAPH_CPU_LIB_NAME
}
)
SET
(
NGRAPH_TBB_LIB
${
NGRAPH_LIB_DIR
}
/
${
NGRAPH_TBB_LIB_NAME
}
)
# Workaround for nGraph expecting mklml to be in mkldnn install directory.
ExternalProject_Add_Step
(
${
NGRAPH_PROJECT
}
...
...
cmake/inference_lib.cmake
浏览文件 @
8c757059
...
...
@@ -129,6 +129,15 @@ if (WITH_MKLDNN)
)
endif
()
if
(
WITH_NGRAPH
)
set
(
dst_dir
"
${
FLUID_INSTALL_DIR
}
/third_party/install/ngraph"
)
copy
(
ngraph_lib
SRCS
${
NGRAPH_INC_DIR
}
${
NGRAPH_LIB_DIR
}
DSTS
${
dst_dir
}
${
dst_dir
}
DEPS ngraph
)
endif
()
if
(
NOT WIN32
)
if
(
NOT MOBILE_INFERENCE AND NOT RPI
)
set
(
dst_dir
"
${
FLUID_INSTALL_DIR
}
/third_party/install/snappy"
)
...
...
paddle/fluid/API.spec
浏览文件 @
8c757059
...
...
@@ -182,7 +182,7 @@ paddle.fluid.layers.clip ArgSpec(args=['x', 'min', 'max', 'name'], varargs=None,
paddle.fluid.layers.clip_by_norm ArgSpec(args=['x', 'max_norm', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.mean ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.mul ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None))
paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', '
name'], varargs=None, keywords=None, defaults=(None,
))
paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', '
ignore_index', 'name'], varargs=None, keywords=None, defaults=(-100, None
))
paddle.fluid.layers.maxout ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.space_to_depth ArgSpec(args=['x', 'blocksize', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.affine_grid ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,))
...
...
@@ -299,6 +299,7 @@ paddle.fluid.layers.generate_proposals ArgSpec(args=['scores', 'bbox_deltas', 'i
paddle.fluid.layers.iou_similarity ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.box_coder ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name'], varargs=None, keywords=None, defaults=('encode_center_size', True, None))
paddle.fluid.layers.polygon_box_transform ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.yolov3_loss ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'class_num', 'ignore_thresh', 'loss_weight_xy', 'loss_weight_wh', 'loss_weight_conf_target', 'loss_weight_conf_notarget', 'loss_weight_class', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None))
paddle.fluid.layers.accuracy ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None))
paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1))
paddle.fluid.layers.exponential_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
...
...
paddle/fluid/framework/CMakeLists.txt
浏览文件 @
8c757059
...
...
@@ -127,8 +127,9 @@ cc_library(version SRCS version.cc)
cc_test
(
version_test SRCS version_test.cc DEPS version
)
cc_library
(
proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS shape_inference op_info operator glog version
)
cc_library
(
ngraph_bridge SRCS ngraph_bridge.cc DEPS operator framework_proto
)
if
(
NOT WIN32
)
cc_library
(
ngraph_bridge SRCS ngraph_bridge.cc DEPS operator framework_proto ngraph
)
cc_library
(
ngraph_operator SRCS ngraph_operator.cc DEPS ngraph_bridge operator op_info device_context tensor scope glog
shape_inference data_transform lod_tensor profiler
)
endif
(
NOT WIN32
)
...
...
paddle/fluid/framework/details/op_registry.h
浏览文件 @
8c757059
...
...
@@ -32,9 +32,7 @@ enum OpInfoFillType {
kOpProtoAndCheckerMaker
=
1
,
kGradOpDescMaker
=
2
,
kVarTypeInference
=
3
,
kShapeInference
=
4
,
kEstimateFlops
=
5
,
kUnknown
=
-
1
kShapeInference
=
4
};
template
<
typename
T
>
...
...
@@ -50,10 +48,8 @@ struct OpInfoFillTypeID {
?
kVarTypeInference
:
(
std
::
is_base_of
<
InferShapeBase
,
T
>::
value
?
kShapeInference
:
(
std
::
is_base_of
<
EstimateFlopsBase
,
T
>::
value
?
kEstimateFlops
:
kUnknown
)))));
:
static_cast
<
OpInfoFillType
>
(
-
1
)))));
}
};
...
...
@@ -143,16 +139,6 @@ struct OpInfoFiller<T, kShapeInference> {
}
};
template
<
typename
T
>
struct
OpInfoFiller
<
T
,
kEstimateFlops
>
{
void
operator
()(
const
char
*
op_tpe
,
OpInfo
*
info
)
const
{
info
->
estimate_flops_
=
[](
InferShapeContext
*
ctx
)
{
T
estimate_flops
;
return
estimate_flops
(
ctx
);
};
}
};
}
// namespace details
}
// namespace framework
...
...
paddle/fluid/framework/ir/is_test_pass.cc
浏览文件 @
8c757059
...
...
@@ -38,7 +38,7 @@ std::unique_ptr<ir::Graph> IsTestPass::ApplyImpl(
for
(
const
Node
*
n
:
graph
->
Nodes
())
{
if
(
n
->
IsOp
())
{
auto
*
op
=
n
->
Op
();
if
(
op
->
HasAttr
(
"is_test"
))
{
if
(
n
->
Runtime
HasAttr
(
"is_test"
))
{
op
->
SetAttr
(
"is_test"
,
true
);
}
else
if
(
std
::
find
(
begin
(
op_list
),
end
(
op_list
),
op
->
Type
())
!=
end
(
op_list
))
{
...
...
paddle/fluid/framework/ir/is_test_pass_tester.cc
浏览文件 @
8c757059
...
...
@@ -104,9 +104,9 @@ TEST(IsTestPass, basic) {
auto
*
op
=
node
->
Op
();
auto
op_name
=
boost
::
get
<
std
::
string
>
(
op
->
GetAttr
(
"name"
));
if
(
op_name
==
"conv3"
)
{
ASSERT_FALSE
(
op
->
HasAttr
(
"is_test"
));
ASSERT_FALSE
(
node
->
Runtime
HasAttr
(
"is_test"
));
}
else
{
ASSERT_TRUE
(
op
->
HasAttr
(
"is_test"
));
ASSERT_TRUE
(
node
->
Runtime
HasAttr
(
"is_test"
));
EXPECT_TRUE
(
boost
::
get
<
bool
>
(
op
->
GetAttr
(
"is_test"
)));
}
}
...
...
paddle/fluid/framework/ir/mkldnn_placement_pass.cc
浏览文件 @
8c757059
...
...
@@ -22,7 +22,7 @@ std::unique_ptr<ir::Graph> MKLDNNPlacementPass::ApplyImpl(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
{
VLOG
(
3
)
<<
"Aplies MKL-DNN placement strategy."
;
for
(
const
Node
*
n
:
graph
->
Nodes
())
{
if
(
n
->
IsOp
()
&&
n
->
Op
()
->
HasAttr
(
"use_mkldnn"
))
{
if
(
n
->
IsOp
()
&&
n
->
Runtime
HasAttr
(
"use_mkldnn"
))
{
n
->
Op
()
->
SetAttr
(
"use_mkldnn"
,
true
);
}
}
...
...
paddle/fluid/framework/ir/node.cc
浏览文件 @
8c757059
...
...
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/ir/node.h"
#include "paddle/fluid/framework/op_info.h"
namespace
paddle
{
namespace
framework
{
...
...
@@ -24,10 +25,33 @@ constexpr char Node::kControlDepVarName[];
const
char
Node
::
kControlDepVarName
[]
=
"__control_var"
;
#endif
std
::
unique_ptr
<
Node
>
CreateNodeForTest
(
const
std
::
string
&
name
,
std
::
unique_ptr
<
Node
>
CreateNodeForTest
(
const
std
::
string
&
name
,
Node
::
Type
type
)
{
return
std
::
unique_ptr
<
Node
>
(
new
Node
(
name
,
type
));
}
bool
Node
::
RuntimeHasAttr
(
const
std
::
string
&
name
)
const
{
if
(
Op
()
->
HasAttr
(
name
))
{
return
true
;
}
else
{
auto
&
op_info
=
OpInfoMap
::
Instance
();
auto
op_type
=
Op
()
->
Type
();
if
(
op_info
.
Has
(
op_type
))
{
auto
op_info_ptr
=
op_info
.
Get
(
op_type
);
if
(
op_info_ptr
.
HasOpProtoAndChecker
())
{
const
proto
::
OpProto
&
proto
=
op_info_ptr
.
Proto
();
for
(
int
i
=
0
;
i
!=
proto
.
attrs_size
();
++
i
)
{
const
proto
::
OpProto
::
Attr
&
attr
=
proto
.
attrs
(
i
);
if
(
attr
.
name
()
==
name
)
{
return
true
;
}
}
}
}
}
return
false
;
}
}
// namespace ir
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/ir/node.h
浏览文件 @
8c757059
...
...
@@ -108,6 +108,18 @@ class Node {
Name
().
find
(
ir
::
Node
::
kControlDepVarName
)
!=
std
::
string
::
npos
;
}
// RuntimeHasAttr is different with HasAttr now.
// 1. For Op()->HasAttr(), it judges whether a stored program_desc_ has attr,
// thus, if stored program_desc_ are old which don't have an attr, a new
// library which adds the attr already will fail on this function.
// Details:
// https://github.com/PaddlePaddle/Paddle/pull/14608#issuecomment-442309087
// 2. For Op()->RuntimeHasAttr, it judges the attr in runtime to avoid above
// problem.
// TODO(luotao): Maybe we should enhance HasAttr later, instead of adding
// RuntimeHasAttr.
bool
RuntimeHasAttr
(
const
std
::
string
&
name
)
const
;
std
::
vector
<
Node
*>
inputs
;
std
::
vector
<
Node
*>
outputs
;
...
...
paddle/fluid/framework/ngraph_bridge.cc
浏览文件 @
8c757059
...
...
@@ -15,23 +15,105 @@ limitations under the License. */
#ifdef PADDLE_WITH_NGRAPH
#include <algorithm>
#include <functional>
#include <vector>
#include "paddle/fluid/framework/ngraph_bridge.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/enforce.h"
#include "ngraph/ngraph.hpp"
namespace
paddle
{
namespace
framework
{
static
std
::
shared_ptr
<
ngraph
::
Node
>
GetNode
(
const
std
::
shared_ptr
<
OperatorBase
>&
op
,
const
std
::
string
prm
,
const
VariableNameMap
&
var_map
,
std
::
shared_ptr
<
std
::
unordered_map
<
std
::
string
,
std
::
shared_ptr
<
ngraph
::
Node
>>>
ngb_node_map
)
{
auto
&
var_names
=
var_map
.
at
(
prm
);
PADDLE_ENFORCE_EQ
(
var_names
.
size
(),
1
,
"op %s prm %s expects one associated var"
,
op
->
Type
(),
prm
);
if
(
ngb_node_map
->
find
(
var_names
[
0
])
!=
ngb_node_map
->
end
())
{
return
(
*
ngb_node_map
)[
var_names
[
0
]];
}
else
{
return
nullptr
;
}
}
static
std
::
shared_ptr
<
ngraph
::
Node
>
GetInputNode
(
const
std
::
shared_ptr
<
OperatorBase
>&
op
,
const
std
::
string
prm
,
std
::
shared_ptr
<
std
::
unordered_map
<
std
::
string
,
std
::
shared_ptr
<
ngraph
::
Node
>>>
ngb_node_map
)
{
return
GetNode
(
op
,
prm
,
op
->
Inputs
(),
ngb_node_map
);
}
static
std
::
shared_ptr
<
ngraph
::
Node
>
GetOutputNode
(
const
std
::
shared_ptr
<
OperatorBase
>&
op
,
const
std
::
string
prm
,
std
::
shared_ptr
<
std
::
unordered_map
<
std
::
string
,
std
::
shared_ptr
<
ngraph
::
Node
>>>
ngb_node_map
)
{
return
GetNode
(
op
,
prm
,
op
->
Outputs
(),
ngb_node_map
);
}
static
void
SetOutputNode
(
const
std
::
shared_ptr
<
OperatorBase
>&
op
,
const
std
::
string
prm
,
std
::
shared_ptr
<
ngraph
::
Node
>
node
,
std
::
shared_ptr
<
std
::
unordered_map
<
std
::
string
,
std
::
shared_ptr
<
ngraph
::
Node
>>>
ngb_node_map
)
{
auto
&
var_names
=
op
->
Outputs
().
at
(
prm
);
if
(
var_names
.
size
()
==
1
)
{
(
*
ngb_node_map
)[
var_names
[
0
]]
=
node
;
}
else
if
(
var_names
.
size
()
==
0
)
{
(
*
ngb_node_map
)[
""
]
=
node
;
}
else
{
PADDLE_THROW
(
"prm %s has more than 1 var_names."
,
prm
);
}
}
static
bool
HasOutput
(
const
std
::
shared_ptr
<
OperatorBase
>&
op
,
const
std
::
string
prm
)
{
auto
&
outputs
=
op
->
Outputs
();
if
(
outputs
.
find
(
prm
)
==
outputs
.
end
())
return
false
;
return
outputs
.
at
(
prm
).
size
()
>
0
;
}
template
<
typename
T
>
static
void
BuildBinaryNode
(
const
std
::
shared_ptr
<
OperatorBase
>&
op
,
std
::
shared_ptr
<
std
::
unordered_map
<
std
::
string
,
std
::
shared_ptr
<
ngraph
::
Node
>>>
ngb_node_map
)
{
auto
x
=
GetInputNode
(
op
,
"X"
,
ngb_node_map
);
auto
y
=
GetInputNode
(
op
,
"Y"
,
ngb_node_map
);
auto
out
=
std
::
make_shared
<
T
>
(
x
,
y
);
SetOutputNode
(
op
,
"Out"
,
out
,
ngb_node_map
);
}
template
<
typename
T
>
static
void
BuildUnaryNode
(
const
std
::
shared_ptr
<
OperatorBase
>&
op
,
std
::
shared_ptr
<
std
::
unordered_map
<
std
::
string
,
std
::
shared_ptr
<
ngraph
::
Node
>>>
ngb_node_map
)
{
auto
input
=
GetInputNode
(
op
,
"X"
,
ngb_node_map
);
auto
out
=
std
::
make_shared
<
T
>
(
input
);
SetOutputNode
(
op
,
"Out"
,
out
,
ngb_node_map
);
}
std
::
map
<
std
::
string
,
std
::
function
<
void
(
const
std
::
shared_ptr
<
OperatorBase
>&
,
std
::
shared_ptr
<
std
::
unordered_map
<
std
::
string
,
std
::
shared_ptr
<
ngraph
::
Node
>>>
)
>>
NgraphBridge
::
NG_NODE_MAP
=
{};
NgraphBridge
::
NG_NODE_MAP
=
{{
"relu"
,
BuildUnaryNode
<
ngraph
::
op
::
Relu
>
},
{
"tanh"
,
BuildUnaryNode
<
ngraph
::
op
::
Tanh
>
}};
void
NgraphBridge
::
build_graph
(
const
std
::
shared_ptr
<
OperatorBase
>&
op
)
{
void
NgraphBridge
::
BuildNgNode
(
const
std
::
shared_ptr
<
OperatorBase
>&
op
)
{
auto
&
op_type
=
op
->
Type
();
NG_NODE_MAP
[
op_type
](
op
,
ngb_node_map
);
NG_NODE_MAP
[
op_type
](
op
,
ngb_node_map
_
);
}
}
// namespace framework
...
...
paddle/fluid/framework/ngraph_bridge.h
浏览文件 @
8c757059
...
...
@@ -20,16 +20,14 @@ limitations under the License. */
#include <map>
#include <string>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/enforce.h"
#include "ngraph/ngraph.hpp"
#include "ngraph/node.hpp"
namespace
paddle
{
namespace
framework
{
class
OperatorBase
;
class
NgraphBridge
{
public:
static
std
::
map
<
...
...
@@ -43,14 +41,14 @@ class NgraphBridge {
std
::
shared_ptr
<
std
::
unordered_map
<
std
::
string
,
std
::
shared_ptr
<
ngraph
::
Node
>>>
var_node_map
)
:
ngb_node_map
(
var_node_map
)
{}
:
ngb_node_map
_
(
var_node_map
)
{}
void
build_graph
(
const
std
::
shared_ptr
<
OperatorBase
>&
op
);
void
BuildNgNode
(
const
std
::
shared_ptr
<
OperatorBase
>&
op
);
private:
std
::
shared_ptr
<
std
::
unordered_map
<
std
::
string
,
std
::
shared_ptr
<
ngraph
::
Node
>>>
ngb_node_map
;
ngb_node_map
_
;
};
}
// namespace framework
...
...
paddle/fluid/framework/ngraph_operator.cc
浏览文件 @
8c757059
...
...
@@ -19,14 +19,29 @@ limitations under the License. */
#include <map>
#include "paddle/fluid/framework/feed_fetch_type.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/ngraph_bridge.h"
#include "paddle/fluid/framework/ngraph_operator.h"
#include "paddle/fluid/framework/
shape_inference
.h"
#include "paddle/fluid/framework/
tensor
.h"
#include "paddle/fluid/framework/var_desc.h"
#include "paddle/fluid/framework/var_type.h"
#include "ngraph/ngraph.hpp"
namespace
paddle
{
namespace
framework
{
static
ngraph
::
Shape
Ddim2Shape
(
const
DDim
&
dims
)
{
ngraph
::
Shape
sp
;
for
(
int
i
=
0
;
i
<
dims
.
size
();
++
i
)
{
int
k
=
dims
[
i
];
k
=
k
==
0
?
1
:
k
;
sp
.
push_back
(
k
);
}
return
sp
;
}
static
std
::
map
<
proto
::
VarType
::
Type
,
ngraph
::
element
::
Type
>
pd2ng_type_map
=
{
{
proto
::
VarType
::
FP32
,
ngraph
::
element
::
f32
},
{
proto
::
VarType
::
FP64
,
ngraph
::
element
::
f64
},
...
...
@@ -42,6 +57,7 @@ typedef enum { /* nGraph support state on ops */
PARTIAL_TEST
/* Support partial list of ops for test */
}
op_state
;
// perform graph build through bridge and execute computation
class
NgraphOperator
{
public:
explicit
NgraphOperator
(
const
Scope
&
scope
,
const
platform
::
Place
&
place
,
...
...
@@ -59,13 +75,23 @@ class NgraphOperator {
persistables_
(
persist
),
fetches_
(
fetches
),
post_op_inputs_
(
post_op_inputs
),
ng_op_state_
(
ng_op_state
)
{}
ng_op_state_
(
ng_op_state
)
{
var_in_node_map_
=
std
::
make_shared
<
std
::
unordered_map
<
std
::
string
,
std
::
shared_ptr
<
ngraph
::
Node
>>>
();
var_node_map_
=
std
::
make_shared
<
std
::
unordered_map
<
std
::
string
,
std
::
shared_ptr
<
ngraph
::
Node
>>>
();
BuildNgIO
();
GetNgFunction
();
}
void
Run
(
const
Scope
&
scope
,
const
platform
::
Place
&
place
)
const
;
private:
static
std
::
unordered_map
<
std
::
string
,
std
::
shared_ptr
<
ngraph
::
Function
>>
func_cache
;
func_cache
_
;
const
Scope
&
scope_
;
const
platform
::
Place
&
place_
;
std
::
vector
<
std
::
shared_ptr
<
OperatorBase
>>
fused_ops_
;
...
...
@@ -74,6 +100,35 @@ class NgraphOperator {
std
::
unordered_set
<
std
::
string
>
fetches_
;
std
::
unordered_set
<
std
::
string
>
post_op_inputs_
;
op_state
ng_op_state_
;
// ngraph backend eg. CPU
static
std
::
shared_ptr
<
ngraph
::
runtime
::
Backend
>
backend_
;
// ngraph function to call and execute
std
::
shared_ptr
<
ngraph
::
Function
>
ngraph_function_
;
// var_name of inputs
std
::
vector
<
std
::
string
>
var_in_
;
// var_name of outputs from fetch in order
std
::
vector
<
std
::
string
>
var_out_
;
// map input vars to nodes
std
::
shared_ptr
<
std
::
unordered_map
<
std
::
string
,
std
::
shared_ptr
<
ngraph
::
Node
>>>
var_in_node_map_
;
// map each var name with a ngraph node
std
::
shared_ptr
<
std
::
unordered_map
<
std
::
string
,
std
::
shared_ptr
<
ngraph
::
Node
>>>
var_node_map_
;
// cache key to check if function is cached
std
::
shared_ptr
<
std
::
string
>
GetCacheKey
();
// get ngraph input and define ngraph input parameters
void
GetNgInputShape
(
std
::
shared_ptr
<
OperatorBase
>
op
);
// Call ngraph bridge to map ops
void
BuildNgNodes
();
// get the ngraph input and output var list
void
BuildNgIO
();
// build ngraph function call
void
BuildNgFunction
();
// Check cache for ngraph function or otherwise build the function
void
GetNgFunction
();
};
std
::
vector
<
std
::
vector
<
std
::
vector
<
std
::
unique_ptr
<
OperatorBase
>>::
iterator
>>
...
...
@@ -86,7 +141,7 @@ FusedOperator::FusedOpIntervals(
}
size_t
size
=
ops
->
size
();
size_t
left
=
0
;
while
(
left
<
size
&&
ops
.
at
(
left
)
->
Type
()
!=
kFeedOpType
)
{
while
(
left
<
size
&&
ops
->
at
(
left
)
->
Type
()
!=
kFeedOpType
)
{
++
left
;
}
if
(
left
==
size
)
{
...
...
@@ -116,7 +171,7 @@ FusedOperator::FusedOpIntervals(
size_t
start
=
pivot
,
end
=
start
;
while
(
pivot
<
right
&&
(
paddle
::
framework
::
NgraphBridge
::
NG_NODE_MAP
.
find
(
ops
.
at
(
pivot
)
->
Type
())
!=
ops
->
at
(
pivot
)
->
Type
())
!=
paddle
::
framework
::
NgraphBridge
::
NG_NODE_MAP
.
end
()))
{
++
pivot
;
++
end
;
...
...
@@ -136,7 +191,9 @@ FusedOperator::FusedOperator(
std
::
vector
<
std
::
unique_ptr
<
OperatorBase
>>::
iterator
end
,
const
std
::
string
&
type
,
const
VariableNameMap
&
inputs
,
const
VariableNameMap
&
outputs
,
const
AttributeMap
&
attrs
)
:
OperatorBase
(
type
,
inputs
,
outputs
,
attrs
),
pdesc
(
prog
),
block
(
block_id
)
{
:
OperatorBase
(
type
,
inputs
,
outputs
,
attrs
),
pdesc_
(
prog
),
block_
(
block_id
)
{
for
(
std
::
vector
<
std
::
unique_ptr
<
OperatorBase
>>::
iterator
it
=
start
;
it
!=
end
;
++
it
)
{
fused_ops_
.
push_back
(
std
::
move
(
*
it
));
...
...
@@ -152,7 +209,7 @@ FusedOperator::FusedOperator(
}
if
((
*
(
start
-
1
))
->
Type
()
==
kFeedOpType
&&
(
*
end
)
->
Type
()
==
kFetchOpType
)
{
is_
complete
=
true
;
is_
full_
=
true
;
}
Process
();
...
...
@@ -205,7 +262,7 @@ void FusedOperator::RunImpl(const Scope& scope,
}
}
if
(
is_full
)
{
if
(
is_full
_
)
{
ng_op_state
=
ng_op_state
==
PARTIAL_TEST
?
FULL_TEST
:
FULL_TRAIN
;
}
...
...
@@ -215,6 +272,280 @@ void FusedOperator::RunImpl(const Scope& scope,
ngraph_op
.
Run
(
scope
,
place
);
}
std
::
unordered_map
<
std
::
string
,
std
::
shared_ptr
<
ngraph
::
Function
>>
NgraphOperator
::
func_cache_
=
{};
std
::
shared_ptr
<
ngraph
::
runtime
::
Backend
>
NgraphOperator
::
backend_
=
ngraph
::
runtime
::
Backend
::
create
(
"CPU"
);
void
NgraphOperator
::
GetNgInputShape
(
std
::
shared_ptr
<
OperatorBase
>
op
)
{
op
->
RuntimeInferShape
(
scope_
,
place_
);
for
(
auto
&
var_name_item
:
op
->
Inputs
())
{
for
(
auto
&
var_name
:
var_name_item
.
second
)
{
auto
*
var
=
scope_
.
FindVar
(
var_name
);
if
(
var
&&
var
->
IsType
<
LoDTensor
>
())
{
auto
*
tensor_pd
=
GetLoDTensorOrSelectedRowsValueFromVar
(
*
var
);
auto
sp
=
Ddim2Shape
(
tensor_pd
->
dims
());
if
(
std
::
find
(
var_in_
.
begin
(),
var_in_
.
end
(),
var_name
)
!=
var_in_
.
end
())
{
if
(
var_node_map_
->
find
(
var_name
)
==
var_node_map_
->
end
())
{
auto
ng_type
=
var_type_map_
.
at
(
var_name
);
auto
prm
=
std
::
make_shared
<
ngraph
::
op
::
Parameter
>
(
ng_type
,
sp
,
true
);
(
*
var_node_map_
)[
var_name
]
=
prm
;
(
*
var_in_node_map_
)[
var_name
]
=
prm
;
}
}
}
}
}
}
void
NgraphOperator
::
BuildNgNodes
()
{
for
(
auto
&
var_name
:
var_out_
)
{
if
(
var_node_map_
->
find
(
var_name
)
==
var_node_map_
->
end
())
{
auto
*
var
=
scope_
.
FindVar
(
var_name
);
if
(
var
&&
var
->
IsType
<
LoDTensor
>
())
{
auto
*
tensor_pd
=
GetLoDTensorOrSelectedRowsValueFromVar
(
*
var
);
auto
&
ddim
=
tensor_pd
->
dims
();
auto
ng_shape
=
Ddim2Shape
(
ddim
);
auto
ng_type
=
var_type_map_
.
at
(
var_name
);
auto
prm
=
std
::
make_shared
<
ngraph
::
op
::
Parameter
>
(
ng_type
,
ng_shape
,
true
);
(
*
var_node_map_
)[
var_name
]
=
prm
;
}
}
}
paddle
::
framework
::
NgraphBridge
ngb
(
var_node_map_
);
for
(
auto
&
op
:
fused_ops_
)
{
ngb
.
BuildNgNode
(
op
);
}
}
void
NgraphOperator
::
BuildNgIO
()
{
std
::
unordered_set
<
std
::
string
>
inputs
;
std
::
unordered_set
<
std
::
string
>
outputs
;
for
(
auto
&
op
:
fused_ops_
)
{
for
(
auto
&
var_name_item
:
op
->
Inputs
())
{
for
(
auto
&
var_name
:
var_name_item
.
second
)
{
inputs
.
insert
(
var_name
);
const
bool
is_output
=
outputs
.
find
(
var_name
)
!=
outputs
.
end
();
if
(
!
is_output
&&
std
::
find
(
var_in_
.
begin
(),
var_in_
.
end
(),
var_name
)
==
var_in_
.
end
())
{
// fill var_in here to keep lhs and rhs order
var_in_
.
push_back
(
var_name
);
}
}
}
if
(
op
->
Type
()
!=
"fill_constant"
)
{
GetNgInputShape
(
op
);
}
for
(
auto
&
var_name_item
:
op
->
Outputs
())
{
PADDLE_ENFORCE_LE
(
var_name_item
.
second
.
size
(),
1
,
"op %s has more than 1 output - Not handling yet"
,
op
->
Type
());
for
(
auto
&
var_name
:
var_name_item
.
second
)
{
outputs
.
insert
(
var_name
);
}
}
}
// var_out.clear();
for
(
auto
&
op
:
fused_ops_
)
{
for
(
auto
&
var_name_item
:
op
->
Outputs
())
{
PADDLE_ENFORCE_LE
(
var_name_item
.
second
.
size
(),
1
,
"op %s has more than 1 output - Not handling yet"
,
op
->
Type
());
for
(
auto
&
var_name
:
var_name_item
.
second
)
{
switch
(
ng_op_state_
)
{
case
PARTIAL_TEST
:
if
(
post_op_inputs_
.
find
(
var_name
)
!=
post_op_inputs_
.
end
()
||
fetches_
.
find
(
var_name
)
!=
fetches_
.
end
())
{
var_out_
.
push_back
(
var_name
);
}
break
;
case
FULL_TEST
:
if
(
fetches_
.
find
(
var_name
)
!=
fetches_
.
end
())
{
var_out_
.
push_back
(
var_name
);
}
break
;
case
PARTIAL_TRAIN
:
if
(
fetches_
.
find
(
var_name
)
!=
fetches_
.
end
()
||
post_op_inputs_
.
find
(
var_name
)
!=
post_op_inputs_
.
end
()
||
persistables_
.
find
(
var_name
)
!=
persistables_
.
end
())
{
var_out_
.
push_back
(
var_name
);
}
break
;
case
FULL_TRAIN
:
if
(
fetches_
.
find
(
var_name
)
!=
fetches_
.
end
()
||
persistables_
.
find
(
var_name
)
!=
persistables_
.
end
())
{
var_out_
.
push_back
(
var_name
);
}
break
;
default:
var_out_
.
push_back
(
var_name
);
}
}
}
}
}
void
NgraphOperator
::
BuildNgFunction
()
{
BuildNgNodes
();
ngraph_function_
=
nullptr
;
ngraph
::
NodeVector
func_outputs
;
ngraph
::
op
::
ParameterVector
func_inputs
;
for
(
auto
&
vo
:
var_out_
)
{
func_outputs
.
push_back
(
var_node_map_
->
at
(
vo
));
}
for
(
auto
&
vi
:
var_in_
)
{
std
::
shared_ptr
<
ngraph
::
op
::
Parameter
>
prm
=
std
::
dynamic_pointer_cast
<
ngraph
::
op
::
Parameter
>
(
var_in_node_map_
->
at
(
vi
));
func_inputs
.
push_back
(
prm
);
}
ngraph_function_
=
std
::
make_shared
<
ngraph
::
Function
>
(
func_outputs
,
func_inputs
);
}
std
::
shared_ptr
<
std
::
string
>
NgraphOperator
::
GetCacheKey
()
{
auto
cache_key
=
std
::
make_shared
<
std
::
string
>
(
""
);
*
cache_key
+=
std
::
to_string
(
fused_ops_
.
size
());
for
(
auto
&
op
:
fused_ops_
)
{
*
cache_key
+=
op
->
Type
();
}
for
(
auto
&
var_name
:
var_in_
)
{
auto
shape
=
var_node_map_
->
at
(
var_name
)
->
get_shape
();
*
cache_key
+=
var_name
;
*
cache_key
+=
var_type_map_
.
at
(
var_name
).
c_type_string
();
for
(
size_t
i
=
0
;
i
<
shape
.
size
();
++
i
)
{
*
cache_key
+=
std
::
to_string
(
shape
.
at
(
i
));
}
}
for
(
auto
&
var_name
:
var_out_
)
{
auto
*
var
=
scope_
.
FindVar
(
var_name
);
if
(
var
&&
var
->
IsType
<
LoDTensor
>
())
{
auto
*
tensor_pd
=
GetLoDTensorOrSelectedRowsValueFromVar
(
*
var
);
auto
&
ddim
=
tensor_pd
->
dims
();
for
(
int
i
=
0
;
i
<
ddim
.
size
();
++
i
)
{
*
cache_key
+=
std
::
to_string
(
ddim
[
i
]);
}
}
}
return
cache_key
;
}
void
NgraphOperator
::
GetNgFunction
()
{
bool
cache_on
=
true
;
if
(
cache_on
)
{
std
::
string
cache_key_val
=
*
GetCacheKey
();
if
(
func_cache_
.
find
(
cache_key_val
)
!=
func_cache_
.
end
())
{
ngraph_function_
=
func_cache_
.
at
(
cache_key_val
);
}
else
{
BuildNgFunction
();
func_cache_
[
cache_key_val
]
=
ngraph_function_
;
}
}
else
{
BuildNgFunction
();
}
}
void
NgraphOperator
::
Run
(
const
Scope
&
scope
,
const
platform
::
Place
&
place
)
const
{
std
::
vector
<
std
::
shared_ptr
<
ngraph
::
runtime
::
Tensor
>>
t_in
;
std
::
vector
<
std
::
shared_ptr
<
ngraph
::
runtime
::
Tensor
>>
t_out
;
for
(
size_t
i
=
0
;
i
<
var_in_
.
size
();
++
i
)
{
auto
vi
=
var_in_
.
at
(
i
);
auto
sp
=
var_node_map_
->
at
(
vi
)
->
get_shape
();
std
::
shared_ptr
<
ngraph
::
runtime
::
Tensor
>
ti
;
auto
*
var
=
scope
.
FindVar
(
vi
);
if
(
var
&&
var
->
IsType
<
LoDTensor
>
())
{
auto
*
tensor_pd
=
GetLoDTensorOrSelectedRowsValueFromVar
(
*
var
);
PADDLE_ENFORCE
(
sp
==
Ddim2Shape
(
tensor_pd
->
dims
()),
"Ensure ngraph tensor layout align with paddle tensor"
);
if
(
tensor_pd
->
type
().
hash_code
()
==
typeid
(
float
).
hash_code
())
{
// NOLINT
const
float
*
arr
=
tensor_pd
->
data
<
float
>
();
ti
=
backend_
->
create_tensor
(
ngraph
::
element
::
f32
,
sp
,
const_cast
<
float
*>
(
arr
));
}
else
if
(
tensor_pd
->
type
().
hash_code
()
==
typeid
(
int
).
hash_code
())
{
// NOLINT
const
int
*
arr
=
tensor_pd
->
data
<
int
>
();
ti
=
backend_
->
create_tensor
(
ngraph
::
element
::
i32
,
sp
,
const_cast
<
int
*>
(
arr
));
}
else
if
(
tensor_pd
->
type
().
hash_code
()
==
typeid
(
int64_t
).
hash_code
())
{
const
int64_t
*
arr
=
tensor_pd
->
data
<
int64_t
>
();
ti
=
backend_
->
create_tensor
(
ngraph
::
element
::
i64
,
sp
,
const_cast
<
int64_t
*>
(
arr
));
}
else
if
(
tensor_pd
->
type
().
hash_code
()
==
typeid
(
double
).
hash_code
())
{
// NOLINT
const
double
*
arr
=
tensor_pd
->
data
<
double
>
();
ti
=
backend_
->
create_tensor
(
ngraph
::
element
::
f64
,
sp
,
const_cast
<
double
*>
(
arr
));
}
else
if
(
tensor_pd
->
type
().
hash_code
()
==
typeid
(
bool
).
hash_code
())
{
// NOLINT
const
bool
*
arr
=
tensor_pd
->
data
<
bool
>
();
ti
=
backend_
->
create_tensor
(
ngraph
::
element
::
boolean
,
sp
,
const_cast
<
bool
*>
(
arr
));
}
else
{
PADDLE_THROW
(
"Data type not handling for var %s"
,
vi
);
}
}
else
{
PADDLE_THROW
(
"Cannot find var or tensor with var name %s"
,
vi
);
}
bool
is_test
=
(
ng_op_state_
==
PARTIAL_TEST
||
ng_op_state_
==
FULL_TEST
)
?
true
:
false
;
bool
is_persistable
=
(
persistables_
.
find
(
vi
)
!=
persistables_
.
end
())
?
true
:
false
;
if
(
is_test
&&
is_persistable
)
{
ti
->
set_stale
(
false
);
}
t_in
.
push_back
(
ti
);
}
for
(
size_t
i
=
0
;
i
<
var_out_
.
size
();
++
i
)
{
auto
var_name
=
var_out_
[
i
];
auto
*
var
=
scope
.
FindVar
(
var_name
);
std
::
shared_ptr
<
ngraph
::
runtime
::
Tensor
>
to
;
if
(
var
&&
var
->
IsType
<
LoDTensor
>
())
{
auto
*
tensor_pd
=
GetMutableLoDTensorOrSelectedRowsValueFromVar
(
var
);
auto
dd
=
tensor_pd
->
dims
();
ngraph
::
Shape
sp
=
Ddim2Shape
(
dd
);
auto
ng_type
=
var_type_map_
.
at
(
var_name
);
if
(
ng_type
==
ngraph
::
element
::
f32
)
{
auto
pd_arr
=
tensor_pd
->
mutable_data
<
float
>
(
place
);
to
=
backend_
->
create_tensor
(
ngraph
::
element
::
f32
,
sp
,
pd_arr
);
}
else
if
(
ng_type
==
ngraph
::
element
::
i64
)
{
auto
pd_arr
=
tensor_pd
->
mutable_data
<
int64_t
>
(
place
);
to
=
backend_
->
create_tensor
(
ngraph
::
element
::
i64
,
sp
,
pd_arr
);
}
else
if
(
ng_type
==
ngraph
::
element
::
f64
)
{
auto
pd_arr
=
tensor_pd
->
mutable_data
<
double
>
(
place
);
to
=
backend_
->
create_tensor
(
ngraph
::
element
::
f64
,
sp
,
pd_arr
);
}
else
if
(
ng_type
==
ngraph
::
element
::
boolean
)
{
auto
pd_arr
=
tensor_pd
->
mutable_data
<
bool
>
(
place
);
to
=
backend_
->
create_tensor
(
ngraph
::
element
::
boolean
,
sp
,
pd_arr
);
}
else
{
PADDLE_THROW
(
"Data type not handled in for var %s"
,
var_name
);
}
t_out
.
push_back
(
to
);
}
else
{
PADDLE_THROW
(
"Cannot find var or tensor with var name %s"
,
var_name
);
}
}
backend_
->
call
(
ngraph_function_
,
t_out
,
t_in
);
}
// NgraphOperator::RunImpl
}
// namespace framework
}
// namespace paddle
#endif
paddle/fluid/framework/ngraph_operator.h
浏览文件 @
8c757059
...
...
@@ -17,24 +17,19 @@ limitations under the License. */
#ifdef PADDLE_WITH_NGRAPH
#include <algorithm>
#include <atomic>
#include <string>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/framework/attribute.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/ngraph_bridge.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/op_kernel_type.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/platform/variant.h"
#include "ngraph/
ngraph
.hpp"
#include "ngraph/
type/element_type
.hpp"
namespace
paddle
{
namespace
framework
{
...
...
paddle/fluid/framework/op_info.h
浏览文件 @
8c757059
...
...
@@ -31,12 +31,6 @@ class InferShapeBase {
virtual
void
operator
()(
InferShapeContext
*
)
const
=
0
;
};
class
EstimateFlopsBase
{
public:
virtual
~
EstimateFlopsBase
()
=
default
;
virtual
size_t
operator
()(
InferShapeContext
*
)
const
=
0
;
};
struct
OpInfo
{
OpCreator
creator_
;
GradOpMakerFN
grad_op_maker_
;
...
...
@@ -44,7 +38,6 @@ struct OpInfo {
OpAttrChecker
*
checker_
{
nullptr
};
InferVarTypeFN
infer_var_type_
;
InferShapeFN
infer_shape_
;
EstimateFlopsFN
estimate_flops_
;
bool
HasOpProtoAndChecker
()
const
{
return
proto_
!=
nullptr
&&
checker_
!=
nullptr
;
...
...
paddle/fluid/framework/operator.cc
浏览文件 @
8c757059
...
...
@@ -695,6 +695,12 @@ static void CheckTensorNANOrInf(const std::string& name,
"Tensor %s contains NAN"
,
name
);
}
void
OperatorWithKernel
::
RuntimeInferShape
(
const
Scope
&
scope
,
const
platform
::
Place
&
place
)
const
{
RuntimeInferShapeContext
infer_shape_ctx
(
*
this
,
scope
);
this
->
InferShape
(
&
infer_shape_ctx
);
}
void
OperatorWithKernel
::
RunImpl
(
const
Scope
&
scope
,
const
platform
::
Place
&
place
)
const
{
RuntimeInferShapeContext
infer_shape_ctx
(
*
this
,
scope
);
...
...
paddle/fluid/framework/operator.h
浏览文件 @
8c757059
...
...
@@ -128,6 +128,8 @@ class OperatorBase {
virtual
std
::
vector
<
std
::
string
>
OutputVars
(
bool
has_intermediate
)
const
;
void
SetIsCalledByExecutor
(
bool
x
)
{
run_by_executor_
=
x
;
}
virtual
void
RuntimeInferShape
(
const
Scope
&
scope
,
const
platform
::
Place
&
place
)
const
{}
protected:
std
::
string
type_
;
...
...
@@ -348,6 +350,9 @@ class OperatorWithKernel : public OperatorBase {
OpInfoMap
::
Instance
().
Get
(
Type
()).
infer_shape_
(
ctx
);
}
void
RuntimeInferShape
(
const
Scope
&
scope
,
const
platform
::
Place
&
place
)
const
override
;
protected:
virtual
OpKernelType
GetExpectedKernelType
(
const
ExecutionContext
&
ctx
)
const
;
virtual
OpKernelType
GetKernelTypeForVar
(
...
...
paddle/fluid/framework/type_defs.h
浏览文件 @
8c757059
...
...
@@ -54,7 +54,5 @@ using InferVarTypeFN =
using
InferShapeFN
=
std
::
function
<
void
(
InferShapeContext
*
)
>
;
using
EstimateFlopsFN
=
std
::
function
<
void
(
InferShapeContext
*
)
>
;
}
// namespace framework
}
// namespace paddle
paddle/fluid/inference/api/demo_ci/CMakeLists.txt
浏览文件 @
8c757059
...
...
@@ -79,6 +79,16 @@ link_directories("${PADDLE_LIB}/third_party/install/gflags/lib")
link_directories
(
"
${
PADDLE_LIB
}
/third_party/install/xxhash/lib"
)
link_directories
(
"
${
PADDLE_LIB
}
/paddle/lib"
)
if
(
NOT WIN32
)
set
(
NGRAPH_PATH
"
${
PADDLE_LIB
}
/third_party/install/ngraph"
)
if
(
EXISTS
${
NGRAPH_PATH
}
)
include
(
GNUInstallDirs
)
include_directories
(
"
${
NGRAPH_PATH
}
/include"
)
link_directories
(
"
${
NGRAPH_PATH
}
/
${
CMAKE_INSTALL_LIBDIR
}
"
)
set
(
NGRAPH_LIB
${
NGRAPH_PATH
}
/
${
CMAKE_INSTALL_LIBDIR
}
/libngraph
${
CMAKE_SHARED_LIBRARY_SUFFIX
}
)
endif
()
endif
()
add_executable
(
${
DEMO_NAME
}
${
DEMO_NAME
}
.cc
)
if
(
WITH_MKL
)
...
...
@@ -106,7 +116,7 @@ endif()
if
(
NOT WIN32
)
set
(
EXTERNAL_LIB
"-lrt -ldl -lpthread"
)
set
(
DEPS
${
DEPS
}
${
MATH_LIB
}
${
MKLDNN_LIB
}
${
MATH_LIB
}
${
MKLDNN_LIB
}
${
NGRAPH_LIB
}
glog gflags protobuf snappystream snappy z xxhash
${
EXTERNAL_LIB
}
)
else
()
...
...
paddle/fluid/operators/sequence_ops/sequence_mask_op.h
浏览文件 @
8c757059
...
...
@@ -36,12 +36,10 @@ class SequenceMaskOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) must exist"
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Y"
),
"Output(Y) must exist"
);
auto
maxlen
=
ctx
->
Attrs
().
Get
<
int
>
(
"maxlen"
);
if
(
maxlen
>
0
)
{
// We can only infershape when maxlen > 0
auto
dim
=
framework
::
vectorize2int
(
ctx
->
GetInputDim
(
"X"
));
dim
.
push_back
(
maxlen
);
ctx
->
SetOutputDim
(
"Y"
,
framework
::
make_ddim
(
dim
));
}
int
maxlen
=
ctx
->
Attrs
().
Get
<
int
>
(
"maxlen"
);
auto
dim
=
framework
::
vectorize2int
(
ctx
->
GetInputDim
(
"X"
));
dim
.
push_back
(
maxlen
>
0
?
maxlen
:
-
1
);
ctx
->
SetOutputDim
(
"Y"
,
framework
::
make_ddim
(
dim
));
}
};
...
...
paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.cc
浏览文件 @
8c757059
...
...
@@ -18,6 +18,7 @@ namespace paddle {
namespace
operators
{
using
framework
::
Tensor
;
const
int
kIgnoreIndex
=
-
100
;
class
SigmoidCrossEntropyWithLogitsOp
:
public
framework
::
OperatorWithKernel
{
public:
...
...
@@ -100,6 +101,11 @@ class SigmoidCrossEntropyWithLogitsOpMaker
AddOutput
(
"Out"
,
"(Tensor, default Tensor<float>), a 2-D tensor with shape N x D "
" of elementwise logistic losses."
);
AddAttr
<
int
>
(
"ignore_index"
,
"(int, default kIgnoreIndex), Specifies a target value that "
"is ignored and"
"does not contribute to the input gradient."
)
.
SetDefault
(
kIgnoreIndex
);
AddComment
(
R"DOC(
SigmoidCrossEntropyWithLogits Operator.
...
...
paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.h
浏览文件 @
8c757059
...
...
@@ -15,33 +15,72 @@ limitations under the License. */
#pragma once
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/hostdevice.h"
#include "paddle/legacy/utils/Logging.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenVector
=
framework
::
EigenVector
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
T
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
T
>
struct
SigmoidCrossEntropyWithLogitsForward
{
HOSTDEVICE
SigmoidCrossEntropyWithLogitsForward
(
const
int
&
ignore_index
)
:
ignore_index
(
ignore_index
)
{}
HOSTDEVICE
T
operator
()(
const
T
&
x
,
const
T
&
label
)
const
{
if
(
static_cast
<
int
>
(
label
)
==
ignore_index
)
{
return
static_cast
<
T
>
(
0.
);
}
T
term1
=
(
x
>
0
)
?
x
:
0
;
T
term2
=
x
*
label
;
T
term3
=
std
::
log
(
static_cast
<
T
>
(
1
)
+
std
::
exp
(
-
(
std
::
abs
(
x
))));
return
term1
-
term2
+
term3
;
}
int
ignore_index
;
};
template
<
typename
T
>
struct
SigmoidCrossEntropyWithLogitsBackward
{
HOSTDEVICE
SigmoidCrossEntropyWithLogitsBackward
(
const
int
&
ignore_index
)
:
ignore_index
(
ignore_index
)
{}
HOSTDEVICE
T
operator
()(
const
T
&
x
,
const
T
&
label
)
const
{
if
(
static_cast
<
int
>
(
label
)
==
ignore_index
)
{
return
static_cast
<
T
>
(
0.
);
}
T
simoid_x
=
static_cast
<
T
>
(
1
)
/
(
static_cast
<
T
>
(
1
)
+
std
::
exp
(
-
x
));
return
simoid_x
-
label
;
}
int
ignore_index
;
};
// Out = max(X, 0) - X * Labels + log(1 + exp(-abs(X)))
template
<
typename
DeviceContext
,
typename
T
>
class
SigmoidCrossEntropyWithLogitsKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
framework
::
Tensor
*
X
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
const
framework
::
Tensor
*
Labels
=
context
.
Input
<
framework
::
Tensor
>
(
"Label"
);
framework
::
Tensor
*
Out
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
const
Tensor
*
X
=
context
.
Input
<
Tensor
>
(
"X"
);
const
Tensor
*
Labels
=
context
.
Input
<
Tensor
>
(
"Label"
);
Tensor
*
Out
=
context
.
Output
<
Tensor
>
(
"Out"
);
Out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
ignore_index
=
context
.
Attr
<
int
>
(
"ignore_index"
);
auto
x
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
X
);
auto
labels
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
Labels
);
auto
out
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
Out
);
auto
x
=
EigenVector
<
T
>::
Flatten
(
*
X
);
auto
labels
=
EigenVector
<
T
>::
Flatten
(
*
Labels
);
auto
out
=
EigenVector
<
T
>::
Flatten
(
*
Out
);
auto
&
place
=
*
context
.
device_context
<
DeviceContext
>
().
eigen_device
();
// term1 = max(x, 0)
auto
term1
=
x
.
cwiseMax
(
static_cast
<
T
>
(
0
));
// term2 = x * labels
auto
term2
=
x
*
labels
;
// term3 = log(1 + exp(-abs(x)))
auto
term3
=
(
static_cast
<
T
>
(
1
)
+
(
-
(
x
.
abs
())).
exp
()).
log
();
out
.
device
(
place
)
=
term1
-
term2
+
term3
;
out
.
device
(
place
)
=
x
.
binaryExpr
(
labels
,
SigmoidCrossEntropyWithLogitsForward
<
T
>
(
ignore_index
));
}
};
...
...
@@ -50,23 +89,23 @@ template <typename DeviceContext, typename T>
class
SigmoidCrossEntropyWithLogitsGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
framework
::
Tensor
*
X
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
const
framework
::
Tensor
*
Labels
=
context
.
Input
<
framework
::
Tensor
>
(
"Label"
);
const
framework
::
Tensor
*
dOut
=
context
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
framework
::
Tensor
*
dX
=
context
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
const
Tensor
*
X
=
context
.
Input
<
Tensor
>
(
"X"
);
const
Tensor
*
Labels
=
context
.
Input
<
Tensor
>
(
"Label"
);
const
Tensor
*
dOut
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
Tensor
*
dX
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
dX
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
x
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
X
);
auto
labels
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
Labels
);
auto
dout
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dOut
);
auto
dx
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dX
);
auto
ignore_index
=
context
.
Attr
<
int
>
(
"ignore_index"
);
auto
x
=
EigenVector
<
T
>::
Flatten
(
*
X
);
auto
labels
=
EigenVector
<
T
>::
Flatten
(
*
Labels
);
auto
dout
=
EigenVector
<
T
>::
Flatten
(
*
dOut
);
auto
dx
=
EigenVector
<
T
>::
Flatten
(
*
dX
);
auto
&
place
=
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
auto
sigmoid_x
=
static_cast
<
T
>
(
1
)
/
(
static_cast
<
T
>
(
1
)
+
(
-
x
).
exp
());
dx
.
device
(
place
)
=
dout
*
(
sigmoid_x
-
labels
);
auto
diff
=
x
.
binaryExpr
(
labels
,
SigmoidCrossEntropyWithLogitsBackward
<
T
>
(
static_cast
<
int
>
(
ignore_index
)));
dx
.
device
(
place
)
=
dout
*
diff
;
}
};
...
...
paddle/fluid/operators/yolov3_loss_op.cc
0 → 100644
浏览文件 @
8c757059
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/yolov3_loss_op.h"
#include "paddle/fluid/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
using
framework
::
Tensor
;
class
Yolov3LossOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of Yolov3LossOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"GTBox"
),
"Input(GTBox) of Yolov3LossOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"GTLabel"
),
"Input(GTLabel) of Yolov3LossOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Loss"
),
"Output(Loss) of Yolov3LossOp should not be null."
);
auto
dim_x
=
ctx
->
GetInputDim
(
"X"
);
auto
dim_gtbox
=
ctx
->
GetInputDim
(
"GTBox"
);
auto
dim_gtlabel
=
ctx
->
GetInputDim
(
"GTLabel"
);
auto
anchors
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"anchors"
);
auto
class_num
=
ctx
->
Attrs
().
Get
<
int
>
(
"class_num"
);
PADDLE_ENFORCE_EQ
(
dim_x
.
size
(),
4
,
"Input(X) should be a 4-D tensor."
);
PADDLE_ENFORCE_EQ
(
dim_x
[
2
],
dim_x
[
3
],
"Input(X) dim[3] and dim[4] should be euqal."
);
PADDLE_ENFORCE_EQ
(
dim_x
[
1
],
anchors
.
size
()
/
2
*
(
5
+
class_num
),
"Input(X) dim[1] should be equal to (anchor_number * (5 "
"+ class_num))."
);
PADDLE_ENFORCE_EQ
(
dim_gtbox
.
size
(),
3
,
"Input(GTBox) should be a 3-D tensor"
);
PADDLE_ENFORCE_EQ
(
dim_gtbox
[
2
],
4
,
"Input(GTBox) dim[2] should be 5"
);
PADDLE_ENFORCE_EQ
(
dim_gtlabel
.
size
(),
2
,
"Input(GTBox) should be a 2-D tensor"
);
PADDLE_ENFORCE_EQ
(
dim_gtlabel
[
0
],
dim_gtbox
[
0
],
"Input(GTBox) and Input(GTLabel) dim[0] should be same"
);
PADDLE_ENFORCE_EQ
(
dim_gtlabel
[
1
],
dim_gtbox
[
1
],
"Input(GTBox) and Input(GTLabel) dim[1] should be same"
);
PADDLE_ENFORCE_GT
(
anchors
.
size
(),
0
,
"Attr(anchors) length should be greater then 0."
);
PADDLE_ENFORCE_EQ
(
anchors
.
size
()
%
2
,
0
,
"Attr(anchors) length should be even integer."
);
PADDLE_ENFORCE_GT
(
class_num
,
0
,
"Attr(class_num) should be an integer greater then 0."
);
std
::
vector
<
int64_t
>
dim_out
({
1
});
ctx
->
SetOutputDim
(
"Loss"
,
framework
::
make_ddim
(
dim_out
));
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
()),
platform
::
CPUPlace
());
}
};
class
Yolov3LossOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"The input tensor of YOLO v3 loss operator, "
"This is a 4-D tensor with shape of [N, C, H, W]."
"H and W should be same, and the second dimention(C) stores"
"box locations, confidence score and classification one-hot"
"key of each anchor box"
);
AddInput
(
"GTBox"
,
"The input tensor of ground truth boxes, "
"This is a 3-D tensor with shape of [N, max_box_num, 5], "
"max_box_num is the max number of boxes in each image, "
"In the third dimention, stores x, y, w, h coordinates, "
"x, y is the center cordinate of boxes and w, h is the "
"width and height and x, y, w, h should be divided by "
"input image height to scale to [0, 1]."
);
AddInput
(
"GTLabel"
,
"The input tensor of ground truth label, "
"This is a 2-D tensor with shape of [N, max_box_num], "
"and each element shoudl be an integer to indicate the "
"box class id."
);
AddOutput
(
"Loss"
,
"The output yolov3 loss tensor, "
"This is a 1-D tensor with shape of [1]"
);
AddAttr
<
int
>
(
"class_num"
,
"The number of classes to predict."
);
AddAttr
<
std
::
vector
<
int
>>
(
"anchors"
,
"The anchor width and height, "
"it will be parsed pair by pair."
);
AddAttr
<
float
>
(
"ignore_thresh"
,
"The ignore threshold to ignore confidence loss."
);
AddAttr
<
float
>
(
"loss_weight_xy"
,
"The weight of x, y location loss."
)
.
SetDefault
(
1.0
);
AddAttr
<
float
>
(
"loss_weight_wh"
,
"The weight of w, h location loss."
)
.
SetDefault
(
1.0
);
AddAttr
<
float
>
(
"loss_weight_conf_target"
,
"The weight of confidence score loss in locations with target object."
)
.
SetDefault
(
1.0
);
AddAttr
<
float
>
(
"loss_weight_conf_notarget"
,
"The weight of confidence score loss in locations without "
"target object."
)
.
SetDefault
(
1.0
);
AddAttr
<
float
>
(
"loss_weight_class"
,
"The weight of classification loss."
)
.
SetDefault
(
1.0
);
AddComment
(
R"DOC(
This operator generate yolov3 loss by given predict result and ground
truth boxes.
The output of previous network is in shape [N, C, H, W], while H and W
should be the same, specify the grid size, each grid point predict given
number boxes, this given number is specified by anchors, it should be
half anchors length, which following will be represented as S. In the
second dimention(the channel dimention), C should be S * (class_num + 5),
class_num is the box categoriy number of source dataset(such as coco),
so in the second dimention, stores 4 box location coordinates x, y, w, h
and confidence score of the box and class one-hot key of each anchor box.
While the 4 location coordinates if $$tx, ty, tw, th$$, the box predictions
correspnd to:
$$
b_x = \sigma(t_x) + c_x
b_y = \sigma(t_y) + c_y
b_w = p_w e^{t_w}
b_h = p_h e^{t_h}
$$
While $$c_x, c_y$$ is the left top corner of current grid and $$p_w, p_h$$
is specified by anchors.
As for confidence score, it is the logistic regression value of IoU between
anchor boxes and ground truth boxes, the score of the anchor box which has
the max IoU should be 1, and if the anchor box has IoU bigger then ignore
thresh, the confidence score loss of this anchor box will be ignored.
Therefore, the yolov3 loss consist of three major parts, box location loss,
confidence score loss, and classification loss. The MSE loss is used for
box location, and binary cross entropy loss is used for confidence score
loss and classification loss.
Final loss will be represented as follow.
$$
loss = \loss_weight_{xy} * loss_{xy} + \loss_weight_{wh} * loss_{wh}
+ \loss_weight_{conf_target} * loss_{conf_target}
+ \loss_weight_{conf_notarget} * loss_{conf_notarget}
+ \loss_weight_{class} * loss_{class}
$$
)DOC"
);
}
};
class
Yolov3LossOpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) should not be null"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Loss"
)),
"Input(Loss@GRAD) should not be null"
);
auto
dim_x
=
ctx
->
GetInputDim
(
"X"
);
if
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)))
{
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
dim_x
);
}
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
()),
platform
::
CPUPlace
());
}
};
class
Yolov3LossGradMaker
:
public
framework
::
SingleGradOpDescMaker
{
public:
using
framework
::
SingleGradOpDescMaker
::
SingleGradOpDescMaker
;
protected:
std
::
unique_ptr
<
framework
::
OpDesc
>
Apply
()
const
override
{
auto
*
op
=
new
framework
::
OpDesc
();
op
->
SetType
(
"yolov3_loss_grad"
);
op
->
SetInput
(
"X"
,
Input
(
"X"
));
op
->
SetInput
(
"GTBox"
,
Input
(
"GTBox"
));
op
->
SetInput
(
"GTLabel"
,
Input
(
"GTLabel"
));
op
->
SetInput
(
framework
::
GradVarName
(
"Loss"
),
OutputGrad
(
"Loss"
));
op
->
SetAttrMap
(
Attrs
());
op
->
SetOutput
(
framework
::
GradVarName
(
"X"
),
InputGrad
(
"X"
));
op
->
SetOutput
(
framework
::
GradVarName
(
"GTBox"
),
{});
op
->
SetOutput
(
framework
::
GradVarName
(
"GTLabel"
),
{});
return
std
::
unique_ptr
<
framework
::
OpDesc
>
(
op
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
yolov3_loss
,
ops
::
Yolov3LossOp
,
ops
::
Yolov3LossOpMaker
,
ops
::
Yolov3LossGradMaker
);
REGISTER_OPERATOR
(
yolov3_loss_grad
,
ops
::
Yolov3LossOpGrad
);
REGISTER_OP_CPU_KERNEL
(
yolov3_loss
,
ops
::
Yolov3LossKernel
<
float
>
,
ops
::
Yolov3LossKernel
<
double
>
);
REGISTER_OP_CPU_KERNEL
(
yolov3_loss_grad
,
ops
::
Yolov3LossGradKernel
<
float
>
,
ops
::
Yolov3LossGradKernel
<
double
>
);
paddle/fluid/operators/yolov3_loss_op.h
0 → 100644
浏览文件 @
8c757059
此差异已折叠。
点击以展开。
paddle/scripts/paddle_build.sh
浏览文件 @
8c757059
...
...
@@ -442,6 +442,8 @@ EOF
make
install
-j
8
if
[
"
$1
"
==
"cp27-cp27m"
]
;
then
pip
install
--user
${
INSTALL_PREFIX
:-
/paddle/build
}
/opt/paddle/share/wheels/
*
.whl
set
-e
python
-c
"import paddle.fluid"
elif
[
"
$1
"
==
"cp35-cp35m"
]
;
then
pip3.5
install
--user
${
INSTALL_PREFIX
:-
/paddle/build
}
/opt/paddle/share/wheels/
*
.whl
elif
[
"
$1
"
==
"cp36-cp36m"
]
;
then
...
...
@@ -449,7 +451,7 @@ EOF
elif
[
"
$1
"
==
"cp37-cp37m"
]
;
then
pip3.7
install
--user
${
INSTALL_PREFIX
:-
/paddle/build
}
/opt/paddle/share/wheels/
*
.whl
fi
if
[[
${
WITH_FLUID_ONLY
:-
OFF
}
==
"OFF"
]]
;
then
paddle version
fi
...
...
python/paddle/fluid/clip.py
浏览文件 @
8c757059
...
...
@@ -134,12 +134,12 @@ class GradientClipByValue(BaseGradientClipAttr):
Examples:
.. code-block:: python
w_param_attrs = ParamAttr(name=None,
initializer=UniformInitializer(low=-1.0, high=1.0, seed=0),
w_param_attrs =
fluid.
ParamAttr(name=None,
initializer=
fluid.initializer.
UniformInitializer(low=-1.0, high=1.0, seed=0),
learning_rate=1.0,
regularizer=L1Decay(1.0),
regularizer=
fluid.regularizer.
L1Decay(1.0),
trainable=True,
clip=GradientClipByValue(-1.0, 1.0))
clip=
fluid.clip.
GradientClipByValue(-1.0, 1.0))
y_predict = fluid.layers.fc(input=x, size=1, param_attr=w_param_attrs)
"""
...
...
@@ -185,12 +185,12 @@ class GradientClipByNorm(BaseGradientClipAttr):
Examples:
.. code-block:: python
w_param_attrs = ParamAttr(name=None,
initializer=UniformInitializer(low=-1.0, high=1.0, seed=0),
w_param_attrs =
flui.
ParamAttr(name=None,
initializer=
fluid.initializer.
UniformInitializer(low=-1.0, high=1.0, seed=0),
learning_rate=1.0,
regularizer=L1Decay(1.0),
regularizer=
fluid.regularizer.
L1Decay(1.0),
trainable=True,
clip=GradientClipByNorm(clip_norm=2.0))
clip=
fluid.clip.
GradientClipByNorm(clip_norm=2.0))
y_predict = fluid.layers.fc(input=x, size=1, param_attr=w_param_attrs)
"""
...
...
python/paddle/fluid/executor.py
浏览文件 @
8c757059
...
...
@@ -20,7 +20,7 @@ import six
from
.framework
import
Program
,
default_main_program
,
Variable
from
.
import
core
__all__
=
[
'Executor'
,
'global_scope'
,
'scope_guard'
,
'_switch_scope'
]
__all__
=
[
'Executor'
,
'global_scope'
,
'scope_guard'
]
g_scope
=
core
.
Scope
()
...
...
@@ -407,16 +407,17 @@ class Executor(object):
Examples:
>>> data = layers.data(name='X', shape=[1], dtype='float32')
>>> hidden = layers.fc(input=data, size=10)
>>> layers.assign(hidden, out)
>>> loss = layers.mean(out)
>>> data = fluid.layers.data(name='X', shape=[1], dtype='float32')
>>> out = fluid.layers.create_tensor(dtype='float32')
>>> hidden = fluid.layers.fc(input=data, size=10)
>>> fluid.layers.assign(hidden,out)
>>> loss = fluid.layers.mean(out)
>>> adam = fluid.optimizer.Adam()
>>> adam.minimize(loss)
>>> adam.minimize(loss)
>>> cpu = core.CPUPlace()
>>> exe = Executor(cpu)
>>> exe.run(default_startup_program())
>>> exe =
fluid.
Executor(cpu)
>>> exe.run(
fluid.
default_startup_program())
>>> x = numpy.random.random(size=(10, 1)).astype('float32')
>>> outs = exe.run(
...
...
python/paddle/fluid/framework.py
浏览文件 @
8c757059
...
...
@@ -89,12 +89,13 @@ def name_scope(prefix=None):
Examples:
.. code-block:: python
with name_scope("encoder"):
...
with name_scope("decoder"):
...
with name_scope("attention"):
...
with name_scope("attention"):
...
"""
# TODO(panyx0718): Only [0-9a-z].
assert
prefix
,
"namescope prefix cannot be empty."
...
...
python/paddle/fluid/layers/detection.py
浏览文件 @
8c757059
...
...
@@ -20,6 +20,7 @@ from __future__ import print_function
from
.layer_function_generator
import
generate_layer_fn
from
.layer_function_generator
import
autodoc
,
templatedoc
from
..layer_helper
import
LayerHelper
from
..framework
import
Variable
from
.
import
tensor
from
.
import
nn
from
.
import
ops
...
...
@@ -46,6 +47,7 @@ __all__ = [
'iou_similarity'
,
'box_coder'
,
'polygon_box_transform'
,
'yolov3_loss'
,
]
...
...
@@ -401,6 +403,113 @@ def polygon_box_transform(input, name=None):
return
output
@
templatedoc
(
op_type
=
"yolov3_loss"
)
def
yolov3_loss
(
x
,
gtbox
,
gtlabel
,
anchors
,
class_num
,
ignore_thresh
,
loss_weight_xy
=
None
,
loss_weight_wh
=
None
,
loss_weight_conf_target
=
None
,
loss_weight_conf_notarget
=
None
,
loss_weight_class
=
None
,
name
=
None
):
"""
${comment}
Args:
x (Variable): ${x_comment}
gtbox (Variable): groud truth boxes, should be in shape of [N, B, 4],
in the third dimenstion, x, y, w, h should be stored
and x, y, w, h should be relative value of input image.
N is the batch number and B is the max box number in
an image.
gtlabel (Variable): class id of ground truth boxes, shoud be ins shape
of [N, B].
anchors (list|tuple): ${anchors_comment}
class_num (int): ${class_num_comment}
ignore_thresh (float): ${ignore_thresh_comment}
loss_weight_xy (float|None): ${loss_weight_xy_comment}
loss_weight_wh (float|None): ${loss_weight_wh_comment}
loss_weight_conf_target (float|None): ${loss_weight_conf_target_comment}
loss_weight_conf_notarget (float|None): ${loss_weight_conf_notarget_comment}
loss_weight_class (float|None): ${loss_weight_class_comment}
name (string): the name of yolov3 loss
Returns:
Variable: A 1-D tensor with shape [1], the value of yolov3 loss
Raises:
TypeError: Input x of yolov3_loss must be Variable
TypeError: Input gtbox of yolov3_loss must be Variable"
TypeError: Input gtlabel of yolov3_loss must be Variable"
TypeError: Attr anchors of yolov3_loss must be list or tuple
TypeError: Attr class_num of yolov3_loss must be an integer
TypeError: Attr ignore_thresh of yolov3_loss must be a float number
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')
gtbox = fluid.layers.data(name='gtbox', shape=[6, 5], dtype='float32')
gtlabel = fluid.layers.data(name='gtlabel', shape=[6, 1], dtype='int32')
anchors = [10, 13, 16, 30, 33, 23]
loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, class_num=80
anchors=anchors, ignore_thresh=0.5)
"""
helper
=
LayerHelper
(
'yolov3_loss'
,
**
locals
())
if
not
isinstance
(
x
,
Variable
):
raise
TypeError
(
"Input x of yolov3_loss must be Variable"
)
if
not
isinstance
(
gtbox
,
Variable
):
raise
TypeError
(
"Input gtbox of yolov3_loss must be Variable"
)
if
not
isinstance
(
gtlabel
,
Variable
):
raise
TypeError
(
"Input gtlabel of yolov3_loss must be Variable"
)
if
not
isinstance
(
anchors
,
list
)
and
not
isinstance
(
anchors
,
tuple
):
raise
TypeError
(
"Attr anchors of yolov3_loss must be list or tuple"
)
if
not
isinstance
(
class_num
,
int
):
raise
TypeError
(
"Attr class_num of yolov3_loss must be an integer"
)
if
not
isinstance
(
ignore_thresh
,
float
):
raise
TypeError
(
"Attr ignore_thresh of yolov3_loss must be a float number"
)
if
name
is
None
:
loss
=
helper
.
create_variable_for_type_inference
(
dtype
=
x
.
dtype
)
else
:
loss
=
helper
.
create_variable
(
name
=
name
,
dtype
=
x
.
dtype
,
persistable
=
False
)
attrs
=
{
"anchors"
:
anchors
,
"class_num"
:
class_num
,
"ignore_thresh"
:
ignore_thresh
,
}
if
loss_weight_xy
is
not
None
and
isinstance
(
loss_weight_xy
,
float
):
self
.
attrs
[
'loss_weight_xy'
]
=
loss_weight_xy
if
loss_weight_wh
is
not
None
and
isinstance
(
loss_weight_wh
,
float
):
self
.
attrs
[
'loss_weight_wh'
]
=
loss_weight_wh
if
loss_weight_conf_target
is
not
None
and
isinstance
(
loss_weight_conf_target
,
float
):
self
.
attrs
[
'loss_weight_conf_target'
]
=
loss_weight_conf_target
if
loss_weight_conf_notarget
is
not
None
and
isinstance
(
loss_weight_conf_notarget
,
float
):
self
.
attrs
[
'loss_weight_conf_notarget'
]
=
loss_weight_conf_notarget
if
loss_weight_class
is
not
None
and
isinstance
(
loss_weight_class
,
float
):
self
.
attrs
[
'loss_weight_class'
]
=
loss_weight_class
helper
.
append_op
(
type
=
'yolov3_loss'
,
inputs
=
{
"X"
:
x
,
"GTBox"
:
gtbox
,
"GTLabel"
:
gtlabel
},
outputs
=
{
'Loss'
:
loss
},
attrs
=
attrs
)
return
loss
@
templatedoc
()
def
detection_map
(
detect_res
,
label
,
...
...
python/paddle/fluid/layers/io.py
浏览文件 @
8c757059
...
...
@@ -943,7 +943,18 @@ def __create_unshared_decorated_reader__(op_type, reader, attrs, name=None):
def
shuffle
(
reader
,
buffer_size
):
"""
Shuffle the reader.
Creates a data reader whose data output is shuffled.
Output from the iterator that created by original reader will be
buffered into shuffle buffer, and then shuffled. The size of shuffle buffer
is determined by argument buf_size.
Args:
param reader: the original reader whose output will be shuffled.
type reader: callable
param buf_size: shuffle buffer size.
type buf_size: int
return: the new reader whose output is shuffled.
rtype: callable
"""
return
__create_unshared_decorated_reader__
(
'create_shuffle_reader'
,
reader
,
{
'buffer_size'
:
int
(
buffer_size
)})
...
...
python/paddle/fluid/layers/learning_rate_scheduler.py
浏览文件 @
8c757059
...
...
@@ -308,13 +308,9 @@ def piecewise_decay(boundaries, values):
def
append_LARS
(
params_grads
,
learning_rate
,
weight_decay
):
"""Applies LARS (LAYER-WISE ADAPTIVE RATE SCALING) to learning rate for
each layer.
```python
learning_rate *= local_gw_ratio * sqrt(sumsq(param))
/ (sqrt(sumsq(gradient))+ weight_decay * sqrt(sumsq(param)))
```
"""
Applies LARS (LAYER-WISE ADAPTIVE RATE SCALING) to learning rate for
each layer.
Args:
learning_rate: A learning rate Variable. This
...
...
@@ -323,6 +319,11 @@ def append_LARS(params_grads, learning_rate, weight_decay):
Returns:
The decayed learning rate
Examples:
.. code-block:: python
learning_rate *= local_gw_ratio * sqrt(sumsq(param))
/ (sqrt(sumsq(gradient))+ weight_decay * sqrt(sumsq(param)))
"""
def
_balanced_weight
(
param_norm
,
grad_norm
):
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
8c757059
...
...
@@ -172,6 +172,8 @@ __all__ = [
'lstm'
,
]
kIgnoreIndex
=
-
100
def
fc
(
input
,
size
,
...
...
@@ -926,7 +928,7 @@ def dynamic_gru(input,
emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim])
hidden_dim = 512
x = fluid.layers.fc(input=emb, size=hidden_dim * 3)
hidden = fluid.layers.dynamic_gru(input=x,
dim
=hidden_dim)
hidden = fluid.layers.dynamic_gru(input=x,
size
=hidden_dim)
"""
helper
=
LayerHelper
(
'gru'
,
**
locals
())
...
...
@@ -1267,7 +1269,7 @@ def dropout(x,
return
out
def
cross_entropy
(
input
,
label
,
soft_label
=
False
,
ignore_index
=
-
100
):
def
cross_entropy
(
input
,
label
,
soft_label
=
False
,
ignore_index
=
kIgnoreIndex
):
"""
**Cross Entropy Layer**
...
...
@@ -1314,7 +1316,7 @@ def cross_entropy(input, label, soft_label=False, ignore_index=-100):
labels. Default: `False`.
ignore_index (int): Specifies a target value that is ignored and does
not contribute to the input gradient. Only valid
if soft_label is set to False. Default:
-100
if soft_label is set to False. Default:
kIgnoreIndex
Returns:
A 2-D tensor with shape [N x 1], the cross entropy loss.
...
...
@@ -3584,6 +3586,7 @@ def beam_search_decode(ids, scores, beam_size, end_id, name=None):
Examples:
.. code-block:: python
# Suppose `ids` and `scores` are LodTensorArray variables reserving
# the selected ids and scores of all steps
finished_ids, finished_scores = layers.beam_search_decode(
...
...
@@ -5081,7 +5084,7 @@ def im2sequence(input,
output.lod = [[4, 4]]
Examples:
Examples:
.. code-block:: python
...
...
@@ -5185,7 +5188,7 @@ def multiplex(inputs, index):
def
softmax_with_cross_entropy
(
logits
,
label
,
soft_label
=
False
,
ignore_index
=
-
100
,
ignore_index
=
kIgnoreIndex
,
numeric_stable_mode
=
False
,
return_softmax
=
False
):
"""
...
...
@@ -5243,7 +5246,7 @@ def softmax_with_cross_entropy(logits,
labels as soft labels. By default, `soft_label` is set to False.
ignore_index (int): Specifies a target value that is ignored and does
not contribute to the input gradient. Only valid
if soft_label is set to False. Default:
-100
if soft_label is set to False. Default:
kIgnoreIndex
numeric_stable_mode (bool): A flag to indicate whether to use a more
numerically stable algorithm. Only valid
when soft_label is False and GPU is used.
...
...
@@ -5868,24 +5871,23 @@ def pad_constant_like(x, y, pad_value=0., name=None):
[[38, 39, 40]],
[[41, 42, 43]]]]
Y.shape = (1, 3, 1, 3)
And
pad_value = -1,
And
pad_value = -1,
Return:
Out = [[[[35, 36, 37],
[-1, -1, -1]],
[[38, 39, 40],
[-1, -1, -1]],
[[41, 42, 43],
[-1, -1, -1]]],
[[[-1, -1, -1],
[-1, -1, -1]],
[[-1, -1, -1],
[-1, -1, -1]],
[[-1, -1, -1],
[-1, -1, -1]]]]
Out.shape = (2, 3, 2, 3)
Return:
Out = [[[[35, 36, 37],
[-1, -1, -1]],
[[38, 39, 40],
[-1, -1, -1]],
[[41, 42, 43],
[-1, -1, -1]]],
[[[-1, -1, -1],
[-1, -1, -1]],
[[-1, -1, -1],
[-1, -1, -1]],
[[-1, -1, -1],
[-1, -1, -1]]]]
Out.shape = (2, 3, 2, 3)
Args:
x (Variable): The input tensor variable.
...
...
@@ -6124,6 +6126,7 @@ def image_resize(input,
Supporting resample methods:
'BILINEAR' : Bilinear interpolation
'NEAREST' : Nearest neighbor interpolation
Args:
...
...
@@ -6779,7 +6782,7 @@ def crop(x, shape=None, offsets=None, name=None):
# or
z = fluid.layers.data(name="z", shape=[3, 5], dtype="float32")
crop = fluid.layers.crop(z, shape=[2, 3])
crop = fluid.layers.crop(z, shape=[
-1,
2, 3])
"""
helper
=
LayerHelper
(
'crop'
,
**
locals
())
...
...
@@ -7060,39 +7063,40 @@ def pad2d(input,
than height-1. And the width dimension has the same condition.
Example:
.. code-block:: text
Given that X is a channel of image from input:
Given that X is a channel of image from input:
X = [[1, 2, 3],
[4, 5, 6]]
X = [[1, 2, 3],
[4, 5, 6]]
Case 0:
Case 0:
paddings = [0, 1, 2, 3],
mode = 'constant'
pad_value = 0
paddings = [0, 1, 2, 3],
mode = 'constant'
pad_value = 0
Out = [[0, 0, 1, 2, 3, 0, 0, 0]
[0, 0, 4, 5, 6, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0]]
Out = [[0, 0, 1, 2, 3, 0, 0, 0]
[0, 0, 4, 5, 6, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0]]
Case 1:
Case 1:
paddings = [0, 1, 2, 1],
mode = 'reflect'
paddings = [0, 1, 2, 1],
mode = 'reflect'
Out = [[3, 2, 1, 2, 3, 2]
[6, 5, 4, 5, 6, 5]
[3, 2, 1, 2, 3, 2]]
Out = [[3, 2, 1, 2, 3, 2]
[6, 5, 4, 5, 6, 5]
[3, 2, 1, 2, 3, 2]]
Case 2:
Case 2:
paddings = [0, 1, 2, 1],
mode = 'edge'
paddings = [0, 1, 2, 1],
mode = 'edge'
Out = [[1, 1, 1, 2, 3, 3]
[4, 4, 4, 5, 6, 6]
[4, 4, 4, 5, 6, 6]]
Out = [[1, 1, 1, 2, 3, 3]
[4, 4, 4, 5, 6, 6]
[4, 4, 4, 5, 6, 6]]
Args:
...
...
@@ -7330,13 +7334,13 @@ def prelu(x, mode, param_attr=None, name=None):
Args:
x (Variable): The input tensor.
param_attr(ParamAttr|None): The parameter attribute for the learnable
weight (alpha).
weight (alpha).
mode (string): The mode for weight sharing. It supports all, channel
and element. all: all elements share same weight
channel:elements in a channel share same weight
element:each element has a weight
and element. all: all elements share same weight
channel:elements in a channel share same weight
element:each element has a weight
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
will be named automatically.
Returns:
Variable: The output tensor with the same shape as input.
...
...
@@ -8415,13 +8419,17 @@ def mul(x, y, x_num_col_dims=1, y_num_col_dims=1, name=None):
@
templatedoc
()
def
sigmoid_cross_entropy_with_logits
(
x
,
label
,
name
=
None
):
def
sigmoid_cross_entropy_with_logits
(
x
,
label
,
ignore_index
=
kIgnoreIndex
,
name
=
None
):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
label(${label_type}): ${label_comment}
ignore_index(&{ignore_index}): ${ignore_index_comment}
name(basestring|None): Name of the output.
Returns:
...
...
@@ -8440,7 +8448,7 @@ def sigmoid_cross_entropy_with_logits(x, label, name=None):
type
=
"sigmoid_cross_entropy_with_logits"
,
inputs
=
{
"X"
:
x
,
"Label"
:
label
},
attrs
=
{},
attrs
=
{
"ignore_index"
:
ignore_index
},
outputs
=
{
"Out"
:
out
})
return
out
...
...
python/paddle/fluid/metrics.py
浏览文件 @
8c757059
...
...
@@ -222,13 +222,13 @@ class Precision(MetricBase):
Examples:
.. code-block:: python
metric = fluid.metrics.Precision()
for pass in range(PASSES):
metric.reset()
for data in train_reader():
loss, preds, labels = exe.run(fetch_list=[cost, preds, labels])
metric.update(preds=preds, labels=labels)
numpy_precision = metric.eval()
metric = fluid.metrics.Precision()
for pass in range(PASSES):
metric.reset()
for data in train_reader():
loss, preds, labels = exe.run(fetch_list=[cost, preds, labels])
metric.update(preds=preds, labels=labels)
numpy_precision = metric.eval()
"""
def
__init__
(
self
,
name
=
None
):
...
...
@@ -267,13 +267,13 @@ class Recall(MetricBase):
Examples:
.. code-block:: python
metric = fluid.metrics.Recall()
for pass in range(PASSES):
metric.reset()
for data in train_reader():
loss, preds, labels = exe.run(fetch_list=[cost, preds, labels])
metric.update(preds=preds, labels=labels)
numpy_recall = metric.eval()
metric = fluid.metrics.Recall()
for pass in range(PASSES):
metric.reset()
for data in train_reader():
loss, preds, labels = exe.run(fetch_list=[cost, preds, labels])
metric.update(preds=preds, labels=labels)
numpy_recall = metric.eval()
"""
def
__init__
(
self
,
name
=
None
):
...
...
@@ -449,8 +449,9 @@ class EditDistance(MetricBase):
distance_evaluator.update(distances, seq_num)
distance, instance_error = distance_evaluator.eval()
In the above example:
In the above example:
'distance' is the average of the edit distance in a pass.
'instance_error' is the instance error rate in a pass.
"""
...
...
python/paddle/fluid/param_attr.py
浏览文件 @
8c757059
...
...
@@ -50,8 +50,9 @@ class ParamAttr(object):
w_param_attrs = fluid.ParamAttr(name="fc_weight",
learning_rate=0.5,
regularizer=fluid.L2Decay(1.0),
regularizer=fluid.
regularizer.
L2Decay(1.0),
trainable=True)
x = fluid.layers.data(name='X', shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=10, param_attr=w_param_attrs)
"""
...
...
python/paddle/fluid/tests/test_detection.py
浏览文件 @
8c757059
...
...
@@ -388,5 +388,18 @@ class TestGenerateProposals(unittest.TestCase):
print
(
rpn_rois
.
shape
)
class
TestYoloDetection
(
unittest
.
TestCase
):
def
test_yolov3_loss
(
self
):
program
=
Program
()
with
program_guard
(
program
):
x
=
layers
.
data
(
name
=
'x'
,
shape
=
[
30
,
7
,
7
],
dtype
=
'float32'
)
gtbox
=
layers
.
data
(
name
=
'gtbox'
,
shape
=
[
10
,
4
],
dtype
=
'float32'
)
gtlabel
=
layers
.
data
(
name
=
'gtlabel'
,
shape
=
[
10
],
dtype
=
'int32'
)
loss
=
layers
.
yolov3_loss
(
x
,
gtbox
,
gtlabel
,
[
10
,
13
,
30
,
13
],
10
,
0.5
)
self
.
assertIsNotNone
(
loss
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
8c757059
...
...
@@ -170,9 +170,10 @@ class TestBook(unittest.TestCase):
with
program_guard
(
program
):
dat
=
layers
.
data
(
name
=
'data'
,
shape
=
[
10
],
dtype
=
'float32'
)
lbl
=
layers
.
data
(
name
=
'label'
,
shape
=
[
10
],
dtype
=
'float32'
)
ignore_index
=
-
1
self
.
assertIsNotNone
(
layers
.
sigmoid_cross_entropy_with_logits
(
x
=
dat
,
label
=
lbl
))
x
=
dat
,
label
=
lbl
,
ignore_index
=
ignore_index
))
print
(
str
(
program
))
def
test_hsigmoid
(
self
):
...
...
python/paddle/fluid/tests/unittests/test_sigmoid_cross_entropy_with_logits_op.py
浏览文件 @
8c757059
...
...
@@ -56,6 +56,40 @@ class TestSigmoidCrossEntropyWithLogitsOp2(OpTest):
"""Test sigmoid_cross_entropy_with_logit_op with probabalistic label
"""
def
setUp
(
self
):
self
.
op_type
=
"sigmoid_cross_entropy_with_logits"
batch_size
=
64
num_classes
=
20
ignore_index
=
-
1
self
.
inputs
=
{
'X'
:
logit
(
np
.
random
.
uniform
(
0
,
1
,
(
batch_size
,
num_classes
))
.
astype
(
"float32"
)),
'Label'
:
np
.
random
.
randint
(
-
1
,
2
,
(
batch_size
,
num_classes
))
.
astype
(
"float32"
)
}
self
.
attrs
=
{
'ignore_index'
:
ignore_index
,
}
# Fw Pass is implemented as elementwise sigmoid followed by
# elementwise logistic loss
# Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
sigmoid_X
=
expit
(
self
.
inputs
[
'X'
])
term1
=
self
.
inputs
[
'Label'
]
*
np
.
log
(
sigmoid_X
)
term2
=
(
1
-
self
.
inputs
[
'Label'
])
*
np
.
log
(
1
-
sigmoid_X
)
out
=
-
term1
-
term2
out
[
np
.
where
(
self
.
inputs
[
'Label'
]
==
ignore_index
)]
=
0
self
.
outputs
=
{
'Out'
:
out
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'Out'
)
class
TestSigmoidCrossEntropyWithLogitsOp3
(
OpTest
):
"""Test sigmoid_cross_entropy_with_logit_op with probabalistic label
"""
def
setUp
(
self
):
self
.
op_type
=
"sigmoid_cross_entropy_with_logits"
batch_size
=
64
...
...
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py
0 → 100644
浏览文件 @
8c757059
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from
__future__
import
division
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
from
paddle.fluid
import
core
def
sigmoid
(
x
):
return
1.0
/
(
1.0
+
np
.
exp
(
-
1.0
*
x
))
def
mse
(
x
,
y
,
num
):
return
((
y
-
x
)
**
2
).
sum
()
/
num
def
bce
(
x
,
y
,
mask
):
x
=
x
.
reshape
((
-
1
))
y
=
y
.
reshape
((
-
1
))
mask
=
mask
.
reshape
((
-
1
))
error_sum
=
0.0
count
=
0
for
i
in
range
(
x
.
shape
[
0
]):
if
mask
[
i
]
>
0
:
error_sum
+=
y
[
i
]
*
np
.
log
(
x
[
i
])
+
(
1
-
y
[
i
])
*
np
.
log
(
1
-
x
[
i
])
count
+=
1
return
error_sum
/
(
-
1.0
*
count
)
def
box_iou
(
box1
,
box2
):
b1_x1
=
box1
[
0
]
-
box1
[
2
]
/
2
b1_x2
=
box1
[
0
]
+
box1
[
2
]
/
2
b1_y1
=
box1
[
1
]
-
box1
[
3
]
/
2
b1_y2
=
box1
[
1
]
+
box1
[
3
]
/
2
b2_x1
=
box2
[
0
]
-
box2
[
2
]
/
2
b2_x2
=
box2
[
0
]
+
box2
[
2
]
/
2
b2_y1
=
box2
[
1
]
-
box2
[
3
]
/
2
b2_y2
=
box2
[
1
]
+
box2
[
3
]
/
2
b1_area
=
(
b1_x2
-
b1_x1
)
*
(
b1_y2
-
b1_y1
)
b2_area
=
(
b2_x2
-
b2_x1
)
*
(
b2_y2
-
b2_y1
)
inter_rect_x1
=
max
(
b1_x1
,
b2_x1
)
inter_rect_y1
=
max
(
b1_y1
,
b2_y1
)
inter_rect_x2
=
min
(
b1_x2
,
b2_x2
)
inter_rect_y2
=
min
(
b1_y2
,
b2_y2
)
inter_area
=
max
(
inter_rect_x2
-
inter_rect_x1
,
0
)
*
max
(
inter_rect_y2
-
inter_rect_y1
,
0
)
return
inter_area
/
(
b1_area
+
b2_area
+
inter_area
)
def
build_target
(
gtboxs
,
gtlabel
,
attrs
,
grid_size
):
n
,
b
,
_
=
gtboxs
.
shape
ignore_thresh
=
attrs
[
"ignore_thresh"
]
anchors
=
attrs
[
"anchors"
]
class_num
=
attrs
[
"class_num"
]
an_num
=
len
(
anchors
)
//
2
obj_mask
=
np
.
zeros
((
n
,
an_num
,
grid_size
,
grid_size
)).
astype
(
'float32'
)
noobj_mask
=
np
.
ones
((
n
,
an_num
,
grid_size
,
grid_size
)).
astype
(
'float32'
)
tx
=
np
.
zeros
((
n
,
an_num
,
grid_size
,
grid_size
)).
astype
(
'float32'
)
ty
=
np
.
zeros
((
n
,
an_num
,
grid_size
,
grid_size
)).
astype
(
'float32'
)
tw
=
np
.
zeros
((
n
,
an_num
,
grid_size
,
grid_size
)).
astype
(
'float32'
)
th
=
np
.
zeros
((
n
,
an_num
,
grid_size
,
grid_size
)).
astype
(
'float32'
)
tconf
=
np
.
zeros
((
n
,
an_num
,
grid_size
,
grid_size
)).
astype
(
'float32'
)
tcls
=
np
.
zeros
(
(
n
,
an_num
,
grid_size
,
grid_size
,
class_num
)).
astype
(
'float32'
)
for
i
in
range
(
n
):
for
j
in
range
(
b
):
if
gtboxs
[
i
,
j
,
:].
sum
()
==
0
:
continue
gt_label
=
gtlabel
[
i
,
j
]
gx
=
gtboxs
[
i
,
j
,
0
]
*
grid_size
gy
=
gtboxs
[
i
,
j
,
1
]
*
grid_size
gw
=
gtboxs
[
i
,
j
,
2
]
*
grid_size
gh
=
gtboxs
[
i
,
j
,
3
]
*
grid_size
gi
=
int
(
gx
)
gj
=
int
(
gy
)
gtbox
=
[
0
,
0
,
gw
,
gh
]
max_iou
=
0
for
k
in
range
(
an_num
):
anchor_box
=
[
0
,
0
,
anchors
[
2
*
k
],
anchors
[
2
*
k
+
1
]]
iou
=
box_iou
(
gtbox
,
anchor_box
)
if
iou
>
max_iou
:
max_iou
=
iou
best_an_index
=
k
if
iou
>
ignore_thresh
:
noobj_mask
[
i
,
best_an_index
,
gj
,
gi
]
=
0
obj_mask
[
i
,
best_an_index
,
gj
,
gi
]
=
1
noobj_mask
[
i
,
best_an_index
,
gj
,
gi
]
=
0
tx
[
i
,
best_an_index
,
gj
,
gi
]
=
gx
-
gi
ty
[
i
,
best_an_index
,
gj
,
gi
]
=
gy
-
gj
tw
[
i
,
best_an_index
,
gj
,
gi
]
=
np
.
log
(
gw
/
anchors
[
2
*
best_an_index
])
th
[
i
,
best_an_index
,
gj
,
gi
]
=
np
.
log
(
gh
/
anchors
[
2
*
best_an_index
+
1
])
tconf
[
i
,
best_an_index
,
gj
,
gi
]
=
1
tcls
[
i
,
best_an_index
,
gj
,
gi
,
gt_label
]
=
1
return
(
tx
,
ty
,
tw
,
th
,
tconf
,
tcls
,
obj_mask
,
noobj_mask
)
def
YoloV3Loss
(
x
,
gtbox
,
gtlabel
,
attrs
):
n
,
c
,
h
,
w
=
x
.
shape
an_num
=
len
(
attrs
[
'anchors'
])
//
2
class_num
=
attrs
[
"class_num"
]
x
=
x
.
reshape
((
n
,
an_num
,
5
+
class_num
,
h
,
w
)).
transpose
((
0
,
1
,
3
,
4
,
2
))
pred_x
=
sigmoid
(
x
[:,
:,
:,
:,
0
])
pred_y
=
sigmoid
(
x
[:,
:,
:,
:,
1
])
pred_w
=
x
[:,
:,
:,
:,
2
]
pred_h
=
x
[:,
:,
:,
:,
3
]
pred_conf
=
sigmoid
(
x
[:,
:,
:,
:,
4
])
pred_cls
=
sigmoid
(
x
[:,
:,
:,
:,
5
:])
tx
,
ty
,
tw
,
th
,
tconf
,
tcls
,
obj_mask
,
noobj_mask
=
build_target
(
gtbox
,
gtlabel
,
attrs
,
x
.
shape
[
2
])
obj_mask_expand
=
np
.
tile
(
np
.
expand_dims
(
obj_mask
,
4
),
(
1
,
1
,
1
,
1
,
int
(
attrs
[
'class_num'
])))
loss_x
=
mse
(
pred_x
*
obj_mask
,
tx
*
obj_mask
,
obj_mask
.
sum
())
loss_y
=
mse
(
pred_y
*
obj_mask
,
ty
*
obj_mask
,
obj_mask
.
sum
())
loss_w
=
mse
(
pred_w
*
obj_mask
,
tw
*
obj_mask
,
obj_mask
.
sum
())
loss_h
=
mse
(
pred_h
*
obj_mask
,
th
*
obj_mask
,
obj_mask
.
sum
())
loss_conf_target
=
bce
(
pred_conf
*
obj_mask
,
tconf
*
obj_mask
,
obj_mask
)
loss_conf_notarget
=
bce
(
pred_conf
*
noobj_mask
,
tconf
*
noobj_mask
,
noobj_mask
)
loss_class
=
bce
(
pred_cls
*
obj_mask_expand
,
tcls
*
obj_mask_expand
,
obj_mask_expand
)
return
attrs
[
'loss_weight_xy'
]
*
(
loss_x
+
loss_y
)
\
+
attrs
[
'loss_weight_wh'
]
*
(
loss_w
+
loss_h
)
\
+
attrs
[
'loss_weight_conf_target'
]
*
loss_conf_target
\
+
attrs
[
'loss_weight_conf_notarget'
]
*
loss_conf_notarget
\
+
attrs
[
'loss_weight_class'
]
*
loss_class
class
TestYolov3LossOp
(
OpTest
):
def
setUp
(
self
):
self
.
loss_weight_xy
=
1.0
self
.
loss_weight_wh
=
1.0
self
.
loss_weight_conf_target
=
1.0
self
.
loss_weight_conf_notarget
=
1.0
self
.
loss_weight_class
=
1.0
self
.
initTestCase
()
self
.
op_type
=
'yolov3_loss'
x
=
np
.
random
.
random
(
size
=
self
.
x_shape
).
astype
(
'float32'
)
gtbox
=
np
.
random
.
random
(
size
=
self
.
gtbox_shape
).
astype
(
'float32'
)
gtlabel
=
np
.
random
.
randint
(
0
,
self
.
class_num
,
self
.
gtbox_shape
[:
2
]).
astype
(
'int32'
)
self
.
attrs
=
{
"anchors"
:
self
.
anchors
,
"class_num"
:
self
.
class_num
,
"ignore_thresh"
:
self
.
ignore_thresh
,
"loss_weight_xy"
:
self
.
loss_weight_xy
,
"loss_weight_wh"
:
self
.
loss_weight_wh
,
"loss_weight_conf_target"
:
self
.
loss_weight_conf_target
,
"loss_weight_conf_notarget"
:
self
.
loss_weight_conf_notarget
,
"loss_weight_class"
:
self
.
loss_weight_class
,
}
self
.
inputs
=
{
'X'
:
x
,
'GTBox'
:
gtbox
,
'GTLabel'
:
gtlabel
}
self
.
outputs
=
{
'Loss'
:
np
.
array
(
[
YoloV3Loss
(
x
,
gtbox
,
gtlabel
,
self
.
attrs
)]).
astype
(
'float32'
)
}
def
test_check_output
(
self
):
place
=
core
.
CPUPlace
()
self
.
check_output_with_place
(
place
,
atol
=
1e-3
)
def
test_check_grad_ignore_gtbox
(
self
):
place
=
core
.
CPUPlace
()
self
.
check_grad_with_place
(
place
,
[
'X'
],
'Loss'
,
no_grad_set
=
set
([
"GTBox"
,
"GTLabel"
]),
max_relative_error
=
0.06
)
def
initTestCase
(
self
):
self
.
anchors
=
[
10
,
13
,
12
,
12
]
self
.
class_num
=
10
self
.
ignore_thresh
=
0.5
self
.
x_shape
=
(
5
,
len
(
self
.
anchors
)
//
2
*
(
5
+
self
.
class_num
),
7
,
7
)
self
.
gtbox_shape
=
(
5
,
10
,
4
)
self
.
loss_weight_xy
=
2.5
self
.
loss_weight_wh
=
0.8
self
.
loss_weight_conf_target
=
1.5
self
.
loss_weight_conf_notarget
=
0.5
self
.
loss_weight_class
=
1.2
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @
8c757059
...
...
@@ -125,13 +125,14 @@ def slice_variable(var_list, slice_count, min_block_size):
class
DistributeTranspilerConfig
(
object
):
"""
slice_var_up (bool): Do Tensor slice for pservers, default is True.
split_method (PSDispatcher): RoundRobin or HashName can be used
try to choose the best method to balance loads for pservers.
min_block_size (int): Minimum splitted element number in block.
According:https://github.com/PaddlePaddle/Paddle/issues/8638#issuecomment-369912156
We can use bandwidth effiently when data size is larger than 2MB.If you
want to change it, please be sure you see the slice_variable function.
Args:
slice_var_up (bool): Do Tensor slice for pservers, default is True.
split_method (PSDispatcher): RoundRobin or HashName can be used
try to choose the best method to balance loads for pservers.
min_block_size (int): Minimum splitted element number in block.
According:https://github.com/PaddlePaddle/Paddle/issues/8638#issuecomment-369912156
We can use bandwidth effiently when data size is larger than 2MB.If you
want to change it, please be sure you see the slice_variable function.
"""
slice_var_up
=
True
...
...
@@ -163,35 +164,35 @@ class DistributeTranspiler(object):
Examples:
.. code-block:: python
# for pserver mode
pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
trainer_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
current_endpoint = "192.168.0.1:6174"
trainer_id = 0
trainers = 4
role = os.getenv("PADDLE_TRAINING_ROLE")
t = fluid.DistributeTranspiler()
t.transpile(
trainer_id, pservers=pserver_endpoints, trainers=trainers)
if role == "PSERVER":
pserver_program = t.get_pserver_program(current_endpoint)
pserver_startup_program = t.get_startup_program(current_endpoint,
# for pserver mode
pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
trainer_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
current_endpoint = "192.168.0.1:6174"
trainer_id = 0
trainers = 4
role = os.getenv("PADDLE_TRAINING_ROLE")
t = fluid.DistributeTranspiler()
t.transpile(
trainer_id, pservers=pserver_endpoints, trainers=trainers)
if role == "PSERVER":
pserver_program = t.get_pserver_program(current_endpoint)
pserver_startup_program = t.get_startup_program(current_endpoint,
pserver_program)
elif role == "TRAINER":
trainer_program = t.get_trainer_program()
# for nccl2 mode
config = fluid.DistributeTranspilerConfig()
config.mode = "nccl2"
t = fluid.DistributeTranspiler(config=config)
t.transpile(trainer_id, workers=workers, current_endpoint=curr_ep)
exe = fluid.ParallelExecutor(
use_cuda,
loss_name=loss_var.name,
num_trainers=len(trainers.split(",)),
trainer_id=trainer_id
)
elif role == "TRAINER":
trainer_program = t.get_trainer_program()
# for nccl2 mode
config = fluid.DistributeTranspilerConfig()
config.mode = "nccl2"
t = fluid.DistributeTranspiler(config=config)
t.transpile(trainer_id, workers=workers, current_endpoint=curr_ep)
exe = fluid.ParallelExecutor(
use_cuda,
loss_name=loss_var.name,
num_trainers=len(trainers.split(",)),
trainer_id=trainer_id
)
"""
def
__init__
(
self
,
config
=
None
):
...
...
python/setup.py.in
浏览文件 @
8c757059
...
...
@@ -165,9 +165,9 @@ if '${WITH_MKL}' == 'ON':
shutil.copy('${MKLML_LIB}', libs_path)
shutil.copy('${MKLML_IOMP_LIB}', libs_path)
package_data['paddle.libs']+=['libmklml_intel' + ext_name,'libiomp5' + ext_name]
if '${
CMAKE_BUILD_TYPE}' == 'Release
':
# only change rpath in Release mode.
if '${WITH_MKLDNN}' == 'ON':
if '${
WITH_MKLDNN}' == 'ON
':
if '${CMAKE_BUILD_TYPE}' == 'Release':
# only change rpath in Release mode.
# TODO(typhoonzero): use install_name_tool to patch mkl libs once
# we can support mkl on mac.
#
...
...
@@ -177,14 +177,19 @@ if '${CMAKE_BUILD_TYPE}' == 'Release':
command = "patchelf --set-rpath '$ORIGIN/' ${MKLDNN_SHARED_LIB}"
if os.system(command) != 0:
raise Exception("patch libmkldnn.so failed, command: %s" % command)
package_data['paddle.libs']+=['libmkldnn.so.0']
shutil.copy('${MKLDNN_SHARED_LIB}', libs_path)
package_data['paddle.libs']+=['libmkldnn.so.0']
shutil.copy('${MKLDNN_SHARED_LIB}', libs_path)
if '${WITH_NGRAPH}' == 'ON':
# only change rpath in Release mode,
# since in Debug mode, nGraph lib may be too large to be changed?
if '${CMAKE_BUILD_TYPE}' == 'Release':
# only change rpath in Release mode.
command = "patchelf --set-rpath '$ORIGIN/' ${NGRAPH_SHARED_LIB}"
if os.system(command) != 0:
raise Exception("patch ${NGRAPH_SHARED_LIB_NAME} failed, command: %s" % command)
if os.name != 'nt':
if "@APPLE@" == "1":
command = "install_name_tool -id \"@loader_path/\" ${NGRAPH_SHARED_LIB}"
else:
command = "patchelf --set-rpath '$ORIGIN/' ${NGRAPH_SHARED_LIB}"
if os.system(command) != 0:
raise Exception("patch ${NGRAPH_SHARED_LIB_NAME} failed, command: %s" % command)
shutil.copy('${NGRAPH_SHARED_LIB}', libs_path)
shutil.copy('${NGRAPH_CPU_LIB}', libs_path)
shutil.copy('${NGRAPH_TBB_LIB}', libs_path)
...
...
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